Merge branch 'dev'

svo/CMakeLists.txt

@@ -2,7 +2,7 @@
 # user build settings
 
 SET(DEBUG FALSE)
-SET(TRACE FALSE)
+SET(TRACE TRUE)
 SET(HAVE_G2O FALSE) 
 
 ################################################################################

svo/Doxyfile

@@ -648,7 +648,7 @@ WARN_LOGFILE           =
 # directories like "/usr/src/myproject". Separate the files or directories
 # with spaces.
 
-INPUT                  = "src", "include/nano_slam"
+INPUT                  = "src", "include/svo"
 
 # This tag can be used to specify the character encoding of the source files
 # that doxygen parses. Internally doxygen uses the UTF-8 encoding, which is

svo/include/svo/frame.h

@@ -17,7 +17,6 @@
 #ifndef SVO_FRAME_H_
 #define SVO_FRAME_H_
 
-#include <algorithm>
 #include <sophus/se3.h>
 #include <vikit/math_utils.h>
 #include <vikit/abstract_camera.h>
@@ -32,7 +31,6 @@ typedef g2o::VertexSE3Expmap g2oFrameSE3;
 namespace svo {
 
 class Point;
-class Frame;
 struct Feature;
 
 typedef list<Feature*> Features;
@@ -57,7 +55,7 @@ public:
   g2oFrameSE3*                  v_kf_;                  //!< Temporary pointer to the g2o node object of the keyframe.
   int                           last_published_ts_;     //!< Timestamp of last publishing.
 
-  Frame(vk::AbstractCamera* cam, const cv::Mat& img, double timestamp=0.0);
+  Frame(vk::AbstractCamera* cam, const cv::Mat& img, double timestamp);
   ~Frame();
 
   /// Initialize new frame and create image pyramid.

svo/include/svo/frame_handler_base.h

@@ -26,13 +26,14 @@
 #include <svo/global.h>
 #include <svo/map.h>
 
-namespace vk {
-  class AbstractCamera;
-  class PerformanceMonitor;
+namespace vk
+{
+class AbstractCamera;
+class PerformanceMonitor;
 }
 
-namespace svo {
-
+namespace svo
+{
 class Point;
 class Matcher;
 class DepthFilter;
@@ -41,9 +42,22 @@ class DepthFilter;
 class FrameHandlerBase : boost::noncopyable
 {
 public:
-  enum Stage { PAUSED, FIRST_FRAME, SECOND_FRAME, DEFAULT_FRAME };
-  enum TrackingQuality { INSUFFICIENT, BAD, GOOD };
-  enum UpdateResult { NO_KEYFRAME, IS_KEYFRAME, FAILURE };
+  enum Stage {
+    STAGE_PAUSED,
+    STAGE_FIRST_FRAME,
+    STAGE_SECOND_FRAME,
+    STAGE_DEFAULT_FRAME
+  };
+  enum TrackingQuality {
+    TRACKING_INSUFFICIENT,
+    TRACKING_BAD,
+    TRACKING_GOOD
+  };
+  enum UpdateResult {
+    RESULT_NO_KEYFRAME,
+    RESULT_IS_KEYFRAME,
+    RESULT_FAILURE
+  };
 
   FrameHandlerBase();
 
@@ -90,13 +104,13 @@ protected:
   DepthFilter* depth_filter_;                   //!< Depth estimation algorithm runs in a parallel thread and is used to initialize new 3D points.
 
   /// Before a frame is processed, this function is called.
-  bool startFrameProcessingCommon();
+  bool startFrameProcessingCommon(const double timestamp);
 
   /// When a frame is finished processing, this function is called.
   int finishFrameProcessingCommon(
-      size_t update_id,
-      UpdateResult dropout,
-      size_t num_observations);
+      const size_t update_id,
+      const UpdateResult dropout,
+      const size_t num_observations);
 
   /// Reset the map and frame handler to start from scratch.
   void resetCommon();

svo/include/svo/frame_handler_mono.h

@@ -34,6 +34,9 @@ public:
   /// Provide an image.
   void addImage(const cv::Mat& img, double timestamp);
 
+  /// Set the first frame (used for synthetic datasets in benchmark node)
+  void setFirstFrame(const FramePtr& first_frame);
+
   /// Get the last frame that has been processed.
   FramePtr lastFrame() { return last_frame_; }
 

svo/src/frame_handler_base.cpp

@@ -28,7 +28,8 @@
 #include <svo/reprojection.h>
 #include <svo/depth_filter.h>
  
-namespace svo {
+namespace svo
+{
 
 // definition of global and static variables which were declared in the header
 #ifdef SVO_TRACE
@@ -37,13 +38,13 @@ vk::PerformanceMonitor* g_permon = NULL;
 
 FrameHandlerBase::
 FrameHandlerBase() :
-  stage_(PAUSED),
+  stage_(STAGE_PAUSED),
   set_reset_(false),
   set_start_(false),
   acc_frame_timings_(10),
   acc_num_obs_(10),
   num_obs_last_(0),
-  tracking_quality_(BAD),
+  tracking_quality_(TRACKING_BAD),
   depth_filter_(NULL)
 {
 #ifdef SVO_TRACE
@@ -52,10 +53,14 @@ FrameHandlerBase() :
   g_permon->addTimer("pyramid_creation");
   g_permon->addTimer("sparse_img_align");
   g_permon->addTimer("reproject");
+  g_permon->addTimer("reproject_kfs");
+  g_permon->addTimer("reproject_candidates");
+  g_permon->addTimer("feature_align");
   g_permon->addTimer("pose_optimizer");
   g_permon->addTimer("point_optimizer");
   g_permon->addTimer("local_ba");
   g_permon->addTimer("tot_time");
+  g_permon->addLog("timestamp");
   g_permon->addLog("img_align_n_tracked");
   g_permon->addLog("repr_n_mps");
   g_permon->addLog("repr_n_new_references");
@@ -90,18 +95,18 @@ FrameHandlerBase::
 }
 
 bool FrameHandlerBase::
-startFrameProcessingCommon()
+startFrameProcessingCommon(const double timestamp)
 {
   if(set_start_)
   {
     resetAll();
-    stage_ = FIRST_FRAME;
-    set_start_ = false;
+    stage_ = STAGE_FIRST_FRAME;
   }
 
-  if(stage_ == PAUSED)
+  if(stage_ == STAGE_PAUSED)
     return false;
 
+  SVO_LOG(timestamp);
   SVO_DEBUG_STREAM("New Frame");
   SVO_START_TIMER("tot_time");
   timer_.start();
@@ -112,14 +117,17 @@ startFrameProcessingCommon()
 }
 
 int FrameHandlerBase::
-finishFrameProcessingCommon(size_t update_id, UpdateResult dropout, size_t num_observations)
+finishFrameProcessingCommon(
+    const size_t update_id,
+    const UpdateResult dropout,
+    const size_t num_observations)
 {
   SVO_DEBUG_STREAM("Frame: "<<update_id<<"\t fps-avg = "<< 1.0/acc_frame_timings_.getMean()<<"\t nObs = "<<acc_num_obs_.getMean());
   SVO_LOG(dropout);
 
   // save processing time to calculate fps
   acc_frame_timings_.push_back(timer_.stop());
-  if(stage_ == DEFAULT_FRAME)
+  if(stage_ == STAGE_DEFAULT_FRAME)
     acc_num_obs_.push_back(num_observations);
   num_obs_last_ = num_observations;
   SVO_STOP_TIMER("tot_time");
@@ -134,7 +142,7 @@ finishFrameProcessingCommon(size_t update_id, UpdateResult dropout, size_t num_o
   #endif
 
   // reset if failure or requested
-  if(dropout == FAILURE || set_reset_)
+  if(dropout == RESULT_FAILURE || set_reset_)
     resetAll();
   return 0;
 }
@@ -143,9 +151,10 @@ void FrameHandlerBase::
 resetCommon()
 {
   map_.reset();
-  stage_ = PAUSED;
+  stage_ = STAGE_PAUSED;
   set_reset_ = false;
-  tracking_quality_ = BAD;
+  set_start_ = false;
+  tracking_quality_ = TRACKING_BAD;
   depth_filter_->reset();
   num_obs_last_ = 0;
   SVO_INFO_STREAM("RESET");
@@ -154,22 +163,22 @@ resetCommon()
 void FrameHandlerBase::
 setTrackingQuality(const size_t num_observations)
 {
-  tracking_quality_ = GOOD;
+  tracking_quality_ = TRACKING_GOOD;
   if(num_observations < Config::qualityMinFts())
   {
     ROS_WARN_STREAM_THROTTLE(0.5, "Tracking less than "<< Config::qualityMinFts() <<" features!");
-    tracking_quality_ = BAD;
+    tracking_quality_ = TRACKING_BAD;
   }
   const int feature_drop = static_cast<int>(num_obs_last_) - num_observations;
   if(feature_drop > Config::qualityMaxFtsDrop())
   {
     ROS_WARN_STREAM("Lost "<< feature_drop <<" features!");
-    tracking_quality_ = BAD;
+    tracking_quality_ = TRACKING_BAD;
   }
   if(num_observations < 20)
   {
     ROS_ERROR_STREAM("Tracking less than 20 features!");
-    tracking_quality_ = INSUFFICIENT;
+    tracking_quality_ = TRACKING_INSUFFICIENT;
   }
 }
 

svo/src/frame_handler_mono.cpp

@@ -43,9 +43,9 @@ FrameHandlerMono::
 {}
 
 void FrameHandlerMono::
-addImage(const cv::Mat& img, double timestamp)
+addImage(const cv::Mat& img, const double timestamp)
 {
-  if(!startFrameProcessingCommon())
+  if(!startFrameProcessingCommon(timestamp))
     return;
 
   // some cleanup from last iteration, can't do before because of visualization
@@ -58,12 +58,12 @@ addImage(const cv::Mat& img, double timestamp)
   SVO_STOP_TIMER("pyramid_creation");
 
   // process frame
-  UpdateResult res = FAILURE;
-  if(stage_ == DEFAULT_FRAME)
+  UpdateResult res = RESULT_FAILURE;
+  if(stage_ == STAGE_DEFAULT_FRAME)
     res = processFrame();
-  else if(stage_ == SECOND_FRAME)
+  else if(stage_ == STAGE_SECOND_FRAME)
     res = processSecondFrame();
-  else if(stage_ == FIRST_FRAME)
+  else if(stage_ == STAGE_FIRST_FRAME)
     res = processFirstFrame();
 
   // set last frame
@@ -74,17 +74,27 @@ addImage(const cv::Mat& img, double timestamp)
   finishFrameProcessingCommon(last_frame_->id_, res, last_frame_->nObs());
 }
 
+void FrameHandlerMono::
+setFirstFrame(const FramePtr& first_frame)
+{
+  resetAll();
+  last_frame_ = first_frame;
+  last_frame_->setKeyframe();
+  map_.addKeyframe(last_frame_);
+  stage_ = STAGE_DEFAULT_FRAME;
+}
+
 FrameHandlerMono::UpdateResult FrameHandlerMono::
 processFirstFrame()
 {
   new_frame_->T_f_w_ = T_f_w_init_;
   if(klt_homography_init_.addFirstFrame(new_frame_) == initialization::FAILURE)
-    return NO_KEYFRAME;
+    return RESULT_NO_KEYFRAME;
   new_frame_->setKeyframe();
   map_.addKeyframe(new_frame_);
-  stage_ = SECOND_FRAME;
+  stage_ = STAGE_SECOND_FRAME;
   SVO_INFO_STREAM("Init: Selected first frame.");
-  return IS_KEYFRAME;
+  return RESULT_IS_KEYFRAME;
 }
 
 FrameHandlerMono::UpdateResult FrameHandlerMono::
@@ -92,9 +102,9 @@ processSecondFrame()
 {
   initialization::InitResult res = klt_homography_init_.addSecondFrame(new_frame_);
   if(res == initialization::FAILURE)
-    return FAILURE;
+    return RESULT_FAILURE;
   else if(res == initialization::NO_KEYFRAME)
-    return NO_KEYFRAME;
+    return RESULT_NO_KEYFRAME;
 
   // two-frame bundle adjustment
 #ifdef USE_BUNDLE_ADJUSTMENT
@@ -108,10 +118,10 @@ processSecondFrame()
   // add frame to map
   new_frame_->setKeyframe();
   map_.addKeyframe(new_frame_);
-  stage_ = DEFAULT_FRAME;
+  stage_ = STAGE_DEFAULT_FRAME;
   klt_homography_init_.reset();
   SVO_INFO_STREAM("Init: Selected second frame, triangulated initial map.");
-  return IS_KEYFRAME;
+  return RESULT_IS_KEYFRAME;
 }
 
 FrameHandlerMono::UpdateResult FrameHandlerMono::
@@ -140,7 +150,7 @@ processFrame()
   SVO_DEBUG_STREAM("Reprojection:\t nPoints = "<<repr_n_mps<<"\t \t nMatches = "<<repr_n_new_references);
   if(repr_n_new_references == 0) {
     SVO_ERROR_STREAM("Not enough matched features.");
-    return FAILURE;
+    return RESULT_FAILURE;
   }
 
   // pose optimization
@@ -155,7 +165,7 @@ processFrame()
   SVO_DEBUG_STREAM("PoseOptimizer:\t ErrInit = "<<sfba_error_init<<"px\t thresh = "<<sfba_thresh);
   SVO_DEBUG_STREAM("PoseOptimizer:\t ErrFin. = "<<sfba_error_final<<"px\t nObsFin. = "<<sfba_n_edges_final);
   if(sfba_n_edges_final < 10)
-    return FAILURE;
+    return RESULT_FAILURE;
 
   // structure optimization
   SVO_START_TIMER("point_optimizer");
@@ -164,14 +174,14 @@ processFrame()
 
   // select keyframe
   setTrackingQuality(sfba_n_edges_final);
-  if(tracking_quality_ == INSUFFICIENT)
-    return FAILURE;
+  if(tracking_quality_ == TRACKING_INSUFFICIENT)
+    return RESULT_FAILURE;
   double depth_mean, depth_min;
   frame_utils::getSceneDepth(*new_frame_, depth_mean, depth_min);
-  if(!needNewKf(depth_mean) || tracking_quality_ == BAD)
+  if(!needNewKf(depth_mean) || tracking_quality_ == TRACKING_BAD)
   {
     depth_filter_->addFrame(new_frame_);
-    return NO_KEYFRAME;
+    return RESULT_NO_KEYFRAME;
   }
 
   // new keyframe selected
@@ -214,7 +224,7 @@ processFrame()
   new_frame_->setKeyframe();
   map_.addKeyframe(new_frame_);
 
-  return IS_KEYFRAME;
+  return RESULT_IS_KEYFRAME;
 }
 
 void FrameHandlerMono::

svo/src/reprojection.cpp

@@ -70,6 +70,7 @@ void reprojectMap(
   resetGrid(grid);
 
   // Identify those Keyframes which share a common field of view.
+  SVO_START_TIMER("reproject_kfs");
   list< pair<FramePtr,double> > close_kfs;
   map.getCloseKeyframes(frame, close_kfs);
 
@@ -103,8 +104,10 @@ void reprojectMap(
         overlap_kfs.back().second++;
     }
   }
+  SVO_STOP_TIMER("reproject_kfs");
 
   // Now project all point candidates
+  SVO_START_TIMER("reproject_candidates");
   {
     boost::unique_lock<boost::mutex> lock(candidate_points.mut_);
     auto it=candidate_points.candidates_.begin();
@@ -122,10 +125,12 @@ void reprojectMap(
       }
       ++it;
     }
-  }
+  } // unlock the mutex when out of scope
+  SVO_STOP_TIMER("reproject_candidates");
 
   // Now we go through each grid cell and select one point to match.
   // At the end, we should have at maximum one reprojected point per cell.
+  SVO_START_TIMER("feature_align");
   n_matches=0; n_trials=0;
   for(size_t i=0; i<grid.cells.size(); ++i)
   {
@@ -136,6 +141,7 @@ void reprojectMap(
     if(n_matches > (size_t) Config::maxFts())
       break;
   }
+  SVO_STOP_TIMER("feature_align");
 }
 
 bool pointQualityComparator(Candidate& lhs, Candidate& rhs)

svo/test/benchmark.csv

@@ -1,307 +1,307 @@
 local_ba,point_optimizer,pose_optimizer,pyramid_creation,reproject,sparse_img_align,tot_time,dropout,img_align_n_tracked,loba_err_fin,loba_err_init,loba_n_erredges_fin,loba_n_erredges_init,n_candidates,repr_n_mps,repr_n_new_references,sfba_error_final,sfba_error_init,sfba_n_edges_final,sfba_thresh
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svo/test/test_depth_filter.cpp

@@ -118,7 +118,7 @@ void DepthFilterTest::testReconstruction(
     if(i == 0)
     {
       // create reference frame and load ground truth depthmap
-      frame_ref_.reset(new svo::Frame(cam_, img));
+      frame_ref_.reset(new svo::Frame(cam_, img, 0.0));
       frame_ref_->T_f_w_ = T_w_f.inverse();
       depth_filter_->addKeyframe(frame_ref_, 3, 1);
       vk::blender_utils::loadBlenderDepthmap(dataset_dir+"/depth/" + (*it).image_name_ + "_0.depth", *cam_, depth_ref_);
@@ -126,7 +126,7 @@ void DepthFilterTest::testReconstruction(
     }
 
     n_converged_seeds_=0;
-    frame_cur_ = svo::FramePtr(new svo::Frame(cam_, img));
+    frame_cur_ = svo::FramePtr(new svo::Frame(cam_, img, 0.0));
     frame_cur_->T_f_w_ = T_w_f.inverse();
     depth_filter_->addFrame(frame_cur_);
     n_converged_per_iteration.push_back(n_converged_seeds_);

svo/test/test_sparse_img_align.cpp

@@ -86,7 +86,7 @@ void SparseImgAlignTest::testSequence(
     if(i==0)
     {
       // set reference frame
-      frame_ref_.reset(new svo::Frame(cam_, img));
+      frame_ref_.reset(new svo::Frame(cam_, img, 0.0));
       frame_ref_->T_f_w_ = T_gt_w;
 
       // load ground-truth depth
@@ -115,7 +115,7 @@ void SparseImgAlignTest::testSequence(
       continue;
     }
 
-    frame_cur_.reset(new svo::Frame(cam_, img));
+    frame_cur_.reset(new svo::Frame(cam_, img, 0.0));
     //frame_cur_->T_f_w_ = frame_ref_->T_f_w_; // start at reference frame
     frame_cur_->T_f_w_ = T_prev_w; // start at last frame
 

svo_analysis/.gitignore

@@ -0,0 +1 @@
+*.pyc

svo_analysis/CMakeLists.txt

@@ -0,0 +1,8 @@
+cmake_minimum_required(VERSION 2.8.3)
+project(svo_analysis)
+
+find_package(catkin REQUIRED COMPONENTS rospy)
+include_directories(${catkin_INCLUDE_DIRS})
+
+catkin_python_setup()
+#catkin_package(CATKIN_DEPENDS sensor_msgs)

svo_analysis/benchmark.py

@@ -1,67 +0,0 @@
-#!/usr/bin/python
-"""
-Created on Sat Aug 10 18:21:47 2013
-
-@author: Christian Forster
-"""
-
-import os
-import yaml
-import time
-
-class RosNode:
-  def __init__(self, package, executable):
-    self.package = package
-    self.executable = executable
-    
-  def run(self, parameter_dictionary):
-    parameter_string = ''
-    for key in parameter_dictionary.keys():
-      parameter_string = parameter_string + ' _' + key + ':=' + str(parameter_dictionary[key])
-    os.system('rosrun ' + self.package + ' ' + self.executable + ' ' + parameter_string)
-
-if __name__=="__main__":
-  
-  # config:  
-  home_dir = os.getenv("HOME")
-  dataset_dir = home_dir+'/Datasets/SlamBenchmark/'
-  algo_dir = home_dir+'/workspace/rpg_slam/svo_ros'
-  trace_dir = home_dir+'/workspace/rpg_slam/svo_analysis/results'
-  platform = 'i7'
-  
-  # load parameters
-  experiment_name = 'asl2_fast'
-  experiment_file = 'experiments/'+experiment_name+'.yaml'
-  experiment_file_stream = open(experiment_file,'r')
-  experiment_dict = yaml.load(experiment_file_stream)
-  
-  dataset_param_file = dataset_dir+experiment_dict['dataset']+'/dataset_params.yaml'
-  dataset_param_file_stream = open(dataset_param_file,'r')
-  dataset_param_dict = yaml.load(dataset_param_file_stream)
-  
-  algo_param_file = algo_dir+'/param/'+experiment_dict['param_settings']+'.param'
-  algo_param_file_stream = open(algo_param_file,'r')
-  algo_param_dict = yaml.load(algo_param_file_stream)
-  
-  params = dict(dataset_param_dict.items() + algo_param_dict.items() + experiment_dict.items())
-  localtime = time.localtime()
-  params['trace_name'] = time.strftime("%Y%m%d_%H%M", localtime)+'_nslam_'+platform+'_'+experiment_name
-  params['trace_dir'] = trace_dir+'/'+params['trace_name']
-  params['run_id'] = 0
-  
-  # create folder for experiment results
-  if not os.path.exists(params['trace_dir']):
-    os.makedirs(params['trace_dir'])
-    
-  # dump experiment params to file:
-  params_dump_file = params['trace_dir']+'/params.yaml'
-  with open(params_dump_file,'w') as outfile:
-    outfile.write( yaml.dump(params, default_flow_style=False) )
-    
-  # start ros node
-  print 'starting ros node'
-  node = RosNode('svo_ros','benchmark')
-  node.run(params)
-  print 'ros node finished processing'
-  
-  

svo_analysis/evaluate.py

@@ -1,47 +0,0 @@
-#!/usr/bin/python
-"""
-Created on Sat Aug 10 18:21:47 2013
-
-@author: Christian Forster
-"""
-
-import argparse
-import csv
-import numpy as np
-from matplotlib import rc
-import scripts.analyse_logs as analyse_logs
-import scripts.analyse_timing as analyse_timing
-
-if __name__=="__main__":
-  
-  # parse command line 
-  parser = argparse.ArgumentParser()
-  parser.add_argument('--trace_name', help='svo tracefile with timings and logs', default='20131121_0930_px7')
-  args = parser.parse_args()
-
-  # config
-  results_dir = 'results/'+args.trace_name
-  trace_file = results_dir+'/'+args.trace_name+'.csv'
-
-  # tracefile
-  data = csv.reader(open(trace_file))
-  fields = data.next()
-  D = dict()
-  for field in fields:
-    D[field] = list()
-
-  # fill dictionary with column values
-  for row in data:
-    for (field, value) in zip(fields, row):
-      D[field].append(float(value))
-
-  # change dictionary values from list to numpy array for easier manipulation
-  for field, value in D.items():
-    D[field] = np.array(D[field])
-
-  # tell matplotlib to use latex font
-  rc('font',**{'family':'serif','serif':['Cardo']})
-  rc('text', usetex=True)
-
-  analyse_logs.analyse_logs(D, results_dir+'/'+args.trace_name)
-  analyse_timing.analyse_timing(D, results_dir+'/'+args.trace_name)

svo_analysis/experiments/flying_room_1_fast.yaml

@@ -0,0 +1,2 @@
+dataset: flying_room_1_rig_1
+param_settings: vo_fast

svo_analysis/package.xml

@@ -0,0 +1,19 @@
+<package>
+
+  <name>svo_analysis</name>
+  <version>0.0.1</version>
+  <description>
+    Python scripts to analyse performance of SVO.
+  </description>
+  <maintainer email="[email protected]">Christian Forster</maintainer>
+  <author>Christian Forster</author>
+  <license>BSD</license>
+  <buildtool_depend>catkin</buildtool_depend>
+
+  <build_depend>rospy</build_depend>
+
+  <run_depend>python-rospkg</run_depend>
+  <run_depend>python-yaml</run_depend>
+  <run_depend>rospy</run_depend>
+
+</package>

svo_analysis/scripts/analyse_logs.py

@@ -1,132 +0,0 @@
-#!/usr/bin/python
-
-import argparse
-import csv
-import numpy as np
-import matplotlib.pyplot as plt
-from matplotlib import rc
-
-def analyse_logs(D, trace_name):
-
-  # identify measurements which result from normal frames and which from keyframes
-  is_kf = np.argwhere( (D['dropout'] == 1) & (D['repr_n_mps'] >= 0))
-  is_frame = np.argwhere(D['repr_n_mps'] >= 0)
-  is_nokf = np.argwhere( (D['dropout'] == 0) & (D['repr_n_mps'] >= 0))
-  n_frames = len(is_frame)
-  n_kfs = len(is_kf)
-  n_nokf = len(is_nokf)
-  fps = 25.0
-  
-  # plot number of reprojected points
-  mean_n_reproj_points = np.mean(D['repr_n_mps'][is_frame]);
-  mean_n_reproj_matches = np.mean(D['repr_n_new_references'][is_frame]);
-
-  plt.figure()
-  plt.plot(np.arange(n_frames), D['repr_n_mps'][is_frame], 'r-', label='reprojected points, avg = %(i).2f'%{'i':mean_n_reproj_points})
-  plt.plot(np.arange(n_frames), D['repr_n_new_references'][is_frame], 'b-', label='reprojected matches, avg = %(i).2f'%{'i':mean_n_reproj_matches})
-  plt.legend()
-  plt.tight_layout()
-  plt.title('Reprojected points asdf')
-  plt.savefig(trace_name+'_reprojection.pdf', bbox_inches="tight")
-
-  # plot number of triangulations
-#  plt.figure(3)
-#  plt.plot(np.arange(n_kfs), D['triang_n_points'][is_kf], 'g-', label='triangulated points, avg %0.2f'%np.mean(D['triang_n_points'][is_kf]))
-#  plt.plot(np.arange(n_kfs), D['triang_n_refs'][is_kf], 'b-', label='new references, avg %0.2f'%np.mean(D['triang_n_refs'][is_kf]))
-#  plt.plot(np.arange(n_kfs), D['triang_n_fails'][is_kf], 'r-', label='failures, avg %0.2f'%np.mean(D['triang_n_fails'][is_kf]))
-#  plt.plot(np.arange(n_kfs), D['triang_n_add_refs'][is_kf], 'm-', label='additional refs, avg %0.2f'%np.mean(D['triang_n_add_refs'][is_kf]))
-#  plt.legend()
-#  plt.title('Triangulation')
-#  plt.savefig(trace_name+'_img_triangulation.png', bbox_inches="tight")
-
-  # plot number of klt tracked and matched points
-  fig = plt.figure(figsize=(8,2))
-  ax = fig.add_subplot(111, xlabel='time [s]', ylabel='no. features')
-  ax.plot(np.arange(n_frames)/fps, D['repr_n_new_references'][is_frame], 'b-', label='number of successful matches')
-  fig.tight_layout()
-  fig.savefig(trace_name+'number_of_features.pdf', bbox_inches="tight")
-
-  # plot median error before and after pose-optimzation and bundle adjustment
-  init_error_avg = np.mean(D['sfba_error_init'][is_frame])
-  opt1_avg = np.mean(D['sfba_error_final'][is_frame])
-
-  fig = plt.figure(figsize=(8,2))
-  ax = fig.add_subplot(111, xlabel='time [s]', ylabel='error [px]')
-  t_max = n_frames
-  ax.plot(np.arange(n_frames)[0:t_max]/fps, D['sfba_error_init'][is_frame][0:t_max], 'r-', label='Initial error')
-  ax.plot(np.arange(n_frames)[0:t_max]/fps, D['sfba_error_final'][is_frame][0:t_max], 'b-', label='Final error')
-  ax.legend(ncol=2)  
-  ax.set_xlim([0, t_max/fps+4])
-  #ax.set_ylim([0, 0.8])
-  fig.tight_layout()
-  fig.savefig(trace_name+'reprojection_error.pdf', bbox_inches="tight")
-  print 'average reprojection error improvement: ' + str(init_error_avg - opt1_avg)
-
-  # plot number of references
-  plt.figure()
-  plt.fill(np.arange(n_frames+1), np.append(D['repr_n_new_references'][is_frame]+D['klt_n_matches'][is_frame],0), 'g-', label='Reprojection')
-  plt.fill(np.arange(n_frames+1), np.append(D['klt_n_tracked'][is_frame],0), 'b-', label='KLT Matches')
-  plt.plot(np.arange(n_frames), D['sfba_n_edges_final'][is_frame], 'r-', label='References after Optimization', linewidth=2.0)
-  plt.legend()
-  plt.tight_layout()
-  plt.title('Pose Optimization')
-  plt.savefig(trace_name+'_img_n_references.pdf', bbox_inches="tight")
-  
-  # plot number of seeds
-  plt.figure()
-  has_seed = np.argwhere(D['seeds_n_tot'] >= 0)
-  plt.fill(np.arange(len(has_seed)+1), np.append(D['seeds_n_tot'][has_seed],0), label='nSeeds', edgecolor='none') 
-  plt.fill(np.arange(len(has_seed)+1), np.append(D['seeds_n_updates'][has_seed]+D['seeds_n_failed_matches'][has_seed],0), label='nFailedMatches', edgecolor='none')
-  plt.fill(np.arange(len(has_seed)+1), np.append(D['seeds_n_updates'][has_seed],0), label='nUpdates', edgecolor='none') 
-  plt.legend()
-  plt.tight_layout()
-  plt.title('Seeds')
-  plt.savefig(trace_name+'_seeds.pdf', bbox_inches="tight")
-
-
-  plt.figure()
-  plt.plot(D['n_candidates'])
-  plt.title('Candidates')
-  plt.show()
-  
-#  plt.figure()
-#  plt.plot())
-
-if __name__=="__main__":
-
-  dataset='20131003_1937_nslam_i7_asl2_fast'
-  n_align_frames = 10
-  save = True
-  results_dir = '../results/'+dataset
-  logfile = results_dir+'/'+dataset+'_0.csv'
-  
-  # parse command line 
-  parser = argparse.ArgumentParser(description='''
-  This script computes the absolute trajectory error from the ground truth trajectory and the estimated trajectory.
-  ''')
-  parser.add_argument('--trace_file', help='nanoslam tracefile with timings and logs', default=logfile)
-  parser.add_argument('--trace_name', help='name of the tracefile, used for saving figures etc.', default=results_dir+'/')
-  args = parser.parse_args()
-
-  # tracefile
-  data = csv.reader(open(args.trace_file))
-  fields = data.next()
-  D = dict()
-  for field in fields:
-    D[field] = list()
-
-  # fill dictionary with column values
-  for row in data:
-    for (field, value) in zip(fields, row):
-      D[field].append(float(value))
-
-  # change dictionary values from list to numpy array for easier manipulation
-  for field, value in D.items():
-    D[field] = np.array(D[field])
-
-  # tell matplotlib to use latex font
-  rc('font',**{'family':'serif','serif':['Cardo']})
-  rc('text', usetex=True)
-
-  analyse_logs(D, args.trace_name)
-

svo_analysis/scripts/analyse_timing.py

@@ -1,97 +0,0 @@
-#!/usr/bin/python
-
-import argparse
-import csv
-import numpy as np
-import matplotlib.pyplot as plt
-from matplotlib import rc
-
-def analyse_timing(D, trace_name):
-
-  # identify measurements which result from normal frames and which from keyframes
-  is_frame = np.argwhere(D['repr_n_mps'] >= 0)
-  n_frames = len(is_frame)
-
-  # plot total time for frame processing
-  avg_time = np.mean(D['t_tot_time'][is_frame])*1000;
-
-  fig = plt.figure(figsize=(8, 3))
-  ax = fig.add_subplot(111, ylabel='processing time [ms]', xlabel='time [s]')
-  ax.plot(np.arange(n_frames), D['t_tot_time'][is_frame]*1000, 'g-', label='total time [ms]')
-  ax.plot(np.arange(n_frames), np.ones(n_frames)*avg_time, 'b--', label=str('%(time).1fms mean time' % {'time': avg_time}))
-  ax.legend()
-  fig.tight_layout()
-  fig.savefig(trace_name+'_img_tot_processing_time.pdf', bbox_inches="tight")
-  
-  fig = plt.figure(figsize=(6,2))
-  ax = fig.add_subplot(111, xlabel='Processing time [ms]')
-  ax.boxplot([ 
-                D['t_tot_time'][is_frame]*1000,
-#                D['t_local_ba'][is_kf]*1000,
-                D['t_pose_optimizer'][is_frame]*1000 + D['t_point_optimizer'][is_frame]*1000,
-                D['t_reproject'][is_frame]*1000,
-                D['t_sparse_img_align'][is_frame]*1000,
-                D['t_pyramid_creation'][is_frame]*1000
-              ], 0,'', vert=0) # 
-               
-  boxplot_labels = [
-                    r'\textbf{Total Motion Estimation: %2.2fms}' % np.median(D['t_tot_time'][is_frame]*1000),
-#                    'Local BA (KF only): %.2fms ' % np.median(D['t_local_ba'][is_kf]*1000),
-                    'Refinement: %2.2fms' % np.median(D['t_pose_optimizer'][is_frame]*1000 + D['t_point_optimizer'][is_frame]*1000),
-                    'Feature Alignment: %2.2fms' % np.median(D['t_reproject'][is_frame]*1000),
-                    'Sparse Image Alignment: %2.2fms' % np.median(D['t_sparse_img_align'][is_frame]*1000),
-                    'Pyramid Creation: %2.2fms' % np.median(D['t_pyramid_creation'][is_frame]*1000) ]
-                   
-  ax.set_yticks(np.arange(len(boxplot_labels))+1)
-  ax.set_yticklabels(boxplot_labels)
-  fig.tight_layout()
-  fig.savefig(trace_name+'_timing_boxplot.pdf', bbox_inches="tight")
-  
-  fig = plt.figure(figsize=(8, 5))
-  ax = fig.add_subplot(111, ylabel='time [ms]')
-  ax.plot(D['t_reproject']*1000, 'r', label='reproject')
-  ax.plot(D['t_reproject_find_pts']*1000, 'g', label='reproject find pts') # ok!
-  ax.plot(D['t_reproject_best']*1000, 'm', label='reproject best')
-  ax.plot(D['t_reproject_candidates']*1000, 'b', label='reproject candidates')
-  ax.legend()
-  fig.tight_layout()
-  fig.savefig(trace_name+'_reprojection.pdf', bbox_inches="tight")
-
-
-if __name__=="__main__":
-
-  # config
-  dataset='20131003_1937_nslam_i7_asl2_fast'
-  results_dir = '../results/'+dataset
-  tracefile = results_dir + '/'+dataset+'_0.csv'
-  
-  # parse command line
-  parser = argparse.ArgumentParser(description='''
-  This script computes the absolute trajectory error from the ground truth trajectory and the estimated trajectory.
-  ''')
-  parser.add_argument('--trace_file', help='nanoslam tracefile with timings and logs', default=tracefile)
-  parser.add_argument('--trace_name', help='name of the tracefile, used for saving figures etc.', default=results_dir)
-  args = parser.parse_args()
-  results_dir = args.trace_name
-  
-  # tracefile
-  data = csv.reader(open(args.trace_file))
-  fields = data.next()
-  D = dict()
-  for field in fields:
-    D[field] = list()
-
-  # fill dictionary with column values
-  for row in data:
-    for (field, value) in zip(fields, row):
-      D[field].append(float(value))
-
-  # change dictionary values from list to numpy array for easier manipulation
-  for field, value in D.items():
-    D[field] = np.array(D[field])
-
-  # tell matplotlib to use latex font
-  rc('font',**{'family':'serif','serif':['Cardo']})
-  rc('text', usetex=True)
-
-  

svo_analysis/scripts/associate.py

@@ -1,67 +0,0 @@
-#!/usr/bin/python
-#
-# Requirements:
-# sudo apt-get install python-argparse
-
-import argparse
-import sys
-import os
-import numpy
-
-
-def read_file_list(filename):
-    file = open(filename)
-    data = file.read()
-    lines = data.replace(","," ").replace("\t"," ").split("\n")
-    list = [[v.strip() for v in line.split(" ") if v.strip()!=""] for line in lines if len(line)>0 and line[0]!="#"]
-    list = [(float(l[0]),l[1:]) for l in list if len(l)>1]
-    return dict(list)
-
-def associate(first_list, second_list,offset,max_difference):
-    # first_list  = groundtruth
-    # second_list = estimate
-    first_keys = first_list.keys()
-    second_keys = second_list.keys()
-    potential_matches = [(abs(a - (b + offset)), a, b)
-                         for a in first_keys
-                         for b in second_keys
-                         if abs(a - (b + offset)) < max_difference]
-
-    potential_matches.sort()
-    matches = []
-    for diff, a, b in potential_matches:
-        if a in first_keys and b in second_keys:
-            first_keys.remove(a)
-            second_keys.remove(b)
-            matches.append((a, b))
-
-    matches.sort()
-
-    return matches
-
-if __name__ == '__main__':
-
-    # parse command line
-    parser = argparse.ArgumentParser(description='''
-    This script takes two data files with timestamps and associates them
-    ''')
-    parser.add_argument('first_file', help='first text file (format: timestamp data)')
-    parser.add_argument('second_file', help='second text file (format: timestamp data)')
-    parser.add_argument('--first_only', help='only output associated lines from first file', action='store_true')
-    parser.add_argument('--offset', help='time offset added to the timestamps of the second file (default: 0.0)',default=0.0)
-    parser.add_argument('--max_difference', help='maximally allowed time difference for matching entries (default: 0.02)',default=0.02)
-    args = parser.parse_args()
-
-    first_list = read_file_list(args.first_file)
-    second_list = read_file_list(args.second_file)
-
-    matches = associate(first_list, second_list,float(args.offset),float(args.max_difference))
-
-    if args.first_only:
-        for a,b in matches:
-            print("%f %s"%(a," ".join(first_list[a])))
-    else:
-        for a,b in matches:
-            print("%f %s %f %s"%(a," ".join(first_list[a]),b-float(args.offset)," ".join(second_list[b])))
-
-

svo_analysis/scripts/benchmark.py

@@ -0,0 +1,78 @@
+#!/usr/bin/python
+"""
+Created on Sat Aug 10 18:21:47 2013
+
+@author: Christian Forster
+"""
+
+import os
+import yaml
+import rospkg
+import argparse
+import time
+import vikit_py.cpu_info as cpu_info
+import vikit_py.ros_node as ros_node
+import evaluate
+
+if __name__=="__main__":
+  
+  # parse command line
+  parser = argparse.ArgumentParser(description='''
+  Runs SVO with the dataset and parameters specified in the provided experiment file.
+  ''')
+  parser.add_argument('experiment_file', help='experiment file in svo_analysis/experiments folder')
+  parser.add_argument('--name', help='experiment name')
+  parser.add_argument('--evaluate', help='evaluate tracefile after running SVO', action='store_true')
+  parser.add_argument('--version', help='version of svo to evaluate', default='svo_ros')
+  parser.add_argument('--executable', help='the executable to be called', default='benchmark')
+  args = parser.parse_args()
+
+  # load experiment parameters
+  expParamsFile = os.path.join(rospkg.RosPack().get_path('svo_analysis'), 
+                               'experiments', args.experiment_file+'.yaml')
+  expParams = yaml.load(open(expParamsFile, 'r'))
+  expParams['time'] = time.strftime("%Y%m%d_%H%M", time.localtime())
+  expParams['platform'] = cpu_info.get_cpu_info()
+  expParams['experiment_name'] = args.name
+  if(expParams['experiment_name'] == None):
+    expParams['experiment_name'] = expParams['time']+'_'+args.version+'_' + args.experiment_file
+
+  # load dataset parameters
+  expParams['dataset_directory'] = os.path.join(rospkg.RosPack().get_path('Datasets'), expParams['dataset'])
+  if not os.path.exists(expParams['dataset_directory']):
+    raise Exception("Provided dataset folder does not exist.")
+  datasetParamsFile = os.path.join(expParams['dataset_directory'], 'dataset_params.yaml')
+  datasetParams = yaml.load(open(datasetParamsFile,'r'))
+  
+  # load algorithm parameters
+  algoParamsFile = os.path.join(rospkg.RosPack().get_path('svo_ros'), 
+                                'param', expParams['param_settings']+'.yaml')
+  algoParams = yaml.load(open(algoParamsFile,'r'))
+
+  # combine all parameters
+  params = dict(expParams.items() + datasetParams.items() + algoParams.items())
+  params['trace_name'] = 'trace'
+  params['trace_dir'] = os.path.join(rospkg.RosPack().get_path('svo_analysis'),
+                                     'results', params['experiment_name'])
+  
+  # create folder for tracing
+  if not os.path.exists(params['trace_dir']):
+    os.makedirs(params['trace_dir'])
+  
+  # dump experiment params to file:
+  params_dump_file = params['trace_dir']+'/params.yaml'
+  with open(params_dump_file,'w') as outfile:
+    outfile.write(yaml.dump(params, default_flow_style=False))
+    
+  # execute ros node
+  node = ros_node.RosNode(args.version, args.executable)
+  node.run(params)
+  
+  # TODO: check if it is a synthetic dataset 
+  evaluate.evaluate_dataset(params['trace_dir'])
+  
+  
+  
+  
+  
+  

svo_analysis/scripts/evaluate.py

@@ -0,0 +1,50 @@
+#!/usr/bin/python
+"""
+Created on Sat Aug 10 18:21:47 2013
+
+@author: Christian Forster
+"""
+
+import os
+import csv
+import numpy as np
+import rospkg
+import argparse
+import svo_analysis.analyse_logs as analyse_logs
+import svo_analysis.analyse_timing as analyse_timing
+import svo_analysis.analyse_trajectory as analyse_trajectory
+
+def evaluate_dataset(trace_dir):
+  
+  # read tracefile
+  data = csv.reader(open(os.path.join(trace_dir, 'trace.csv')))
+  fields = data.next()
+  D = dict()
+  for field in fields:
+    D[field] = list()
+
+  # fill dictionary with column values
+  for row in data:
+    for (field, value) in zip(fields, row):
+      D[field].append(float(value))
+
+  # change dictionary values from list to numpy array for easier manipulation
+  for field, value in D.items():
+    D[field] = np.array(D[field])
+
+  # generate plots
+  analyse_logs.analyse_logs(D, trace_dir)
+  analyse_timing.analyse_timing(D, trace_dir)
+  analyse_trajectory.analyse_trajectory(trace_dir)
+
+if __name__=="__main__":
+  parser = argparse.ArgumentParser(description='''
+  Evaluates tracefiles of SVO and generates the plots.
+  ''')
+  parser.add_argument('experiment_name', help='directory name of the tracefiles')
+  args = parser.parse_args()
+
+  trace_dir = os.path.join(rospkg.RosPack().get_path('svo_analysis'), 
+                           'results',
+                           args.experiment_name)
+  evaluate_dataset(trace_dir)

svo_analysis/scripts/rotation_utils.py

@@ -1,143 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Created on Fri Jun 28 10:42:37 2013
-
-@author: cforster
-"""
-
-import math
-import numpy as np
-
-# epsilon for testing whether a number is close to zero
-_EPS = np.finfo(float).eps * 4.0
-
-# axis sequences for Euler angles
-_NEXT_AXIS = [1, 2, 0, 1]
-
-# map axes strings to/from tuples of inner axis, parity, repetition, frame
-_AXES2TUPLE = {
-    'sxyz': (0, 0, 0, 0), 'sxyx': (0, 0, 1, 0), 'sxzy': (0, 1, 0, 0),
-    'sxzx': (0, 1, 1, 0), 'syzx': (1, 0, 0, 0), 'syzy': (1, 0, 1, 0),
-    'syxz': (1, 1, 0, 0), 'syxy': (1, 1, 1, 0), 'szxy': (2, 0, 0, 0),
-    'szxz': (2, 0, 1, 0), 'szyx': (2, 1, 0, 0), 'szyz': (2, 1, 1, 0),
-    'rzyx': (0, 0, 0, 1), 'rxyx': (0, 0, 1, 1), 'ryzx': (0, 1, 0, 1),
-    'rxzx': (0, 1, 1, 1), 'rxzy': (1, 0, 0, 1), 'ryzy': (1, 0, 1, 1),
-    'rzxy': (1, 1, 0, 1), 'ryxy': (1, 1, 1, 1), 'ryxz': (2, 0, 0, 1),
-    'rzxz': (2, 0, 1, 1), 'rxyz': (2, 1, 0, 1), 'rzyz': (2, 1, 1, 1)}
-
-_TUPLE2AXES = dict((v, k) for k, v in _AXES2TUPLE.items())
-
-
-def quat2dcm(quaternion):
-    """Convert Quaternion [x y z w] to Direct Cosine Matrix representation """
-    q = np.array(quaternion[:4], dtype=np.float64, copy=True)
-    nq = np.dot(q, q)
-    if nq < _EPS:
-      return np.identity(4)
-    q *= math.sqrt(2.0 / nq)
-    q = np.outer(q, q)
-    return np.array([ [1.0-q[1, 1]-q[2, 2],     q[0, 1]-q[2, 3],     q[0, 2]+q[1, 3]],
-                      [    q[0, 1]+q[2, 3], 1.0-q[0, 0]-q[2, 2],     q[1, 2]-q[0, 3]],
-                      [    q[0, 2]-q[1, 3],     q[1, 2]+q[0, 3], 1.0-q[0, 0]-q[1, 1]]],
-                      dtype=np.float64)
-        
-def dcm2quat(matrix):
-    """Convert Direct Cosine Matrix to Quaternion
-    
-       This algorithm comes from  "Quaternion Calculus and Fast Animation",
-       Ken Shoemake, 1987 SIGGRAPH course notes
-    """
-    q = np.empty((4, ), dtype=np.float64)
-    M = np.array(matrix, dtype=np.float64, copy=False)[:3, :3]
-    t = np.trace(M)
-    if (t > 0):
-      t = math.sqrt(t + 1.0);
-      q[3] = 0.5*t;
-      t = 0.5/t;
-      q[0] = (M[2,1] - M[1,2]) * t;
-      q[1] = (M[0,2] - M[2,0]) * t;
-      q[2] = (M[1,0] - M[0,1]) * t;
-    else:
-      i = 0;
-      if (M[1,1] > M[0,0]):
-        i = 1;
-      if (M[2,2] > M[i,i]):
-        i = 2;
-      j = (i+1)%3;
-      k = (j+1)%3;
-
-      t = math.sqrt(M[i,i]-M[j,j]-M[k,k] + 1.0);
-      q[i] = 0.5 * t;
-      t = 0.5/t;
-      q[3] = (M[k,j]-M[j,k])*t;
-      q[j] = (M[j,i]+M[i,j])*t;
-      q[k] = (M[k,i]+M[i,k])*t;
-    return q
-
-def dcm2rpy(matrix, axes='sxyz'):
-    """Return Euler angles from rotation matrix for specified axis sequence.
-
-    axes : One of 24 axis sequences as string or encoded tuple
-
-    Note that many Euler angle triplets can describe one matrix.
-
-    >>> R0 = euler_matrix(1, 2, 3, 'syxz')
-    >>> al, be, ga = euler_from_matrix(R0, 'syxz')
-    >>> R1 = euler_matrix(al, be, ga, 'syxz')
-    >>> numpy.allclose(R0, R1)
-    True
-    >>> angles = (4.0*math.pi) * (numpy.random.random(3) - 0.5)
-    >>> for axes in _AXES2TUPLE.keys():
-    ...    R0 = euler_matrix(axes=axes, *angles)
-    ...    R1 = euler_matrix(axes=axes, *euler_from_matrix(R0, axes))
-    ...    if not numpy.allclose(R0, R1): print axes, "failed"
-
-    """
-    try:
-        firstaxis, parity, repetition, frame = _AXES2TUPLE[axes.lower()]
-    except (AttributeError, KeyError):
-        _ = _TUPLE2AXES[axes]
-        firstaxis, parity, repetition, frame = axes
-
-    i = firstaxis
-    j = _NEXT_AXIS[i+parity]
-    k = _NEXT_AXIS[i-parity+1]
-
-    M = np.array(matrix, dtype=np.float64, copy=False)[:3, :3]
-    if repetition:
-        sy = math.sqrt(M[i, j]*M[i, j] + M[i, k]*M[i, k])
-        if sy > _EPS:
-            ax = math.atan2( M[i, j],  M[i, k])
-            ay = math.atan2( sy,       M[i, i])
-            az = math.atan2( M[j, i], -M[k, i])
-        else:
-            ax = math.atan2(-M[j, k],  M[j, j])
-            ay = math.atan2( sy,       M[i, i])
-            az = 0.0
-    else:
-        cy = math.sqrt(M[i, i]*M[i, i] + M[j, i]*M[j, i])
-        if cy > _EPS:
-            ax = math.atan2( M[k, j],  M[k, k])
-            ay = math.atan2(-M[k, i],  cy)
-            az = math.atan2( M[j, i],  M[i, i])
-        else:
-            ax = math.atan2(-M[j, k],  M[j, j])
-            ay = math.atan2(-M[k, i],  cy)
-            az = 0.0
-
-    if parity:
-        ax, ay, az = -ax, -ay, -az
-    if frame:
-        ax, az = az, ax
-    return ax, ay, az
-    
-def quat2rpy(quaternion, axes='sxyz'):
-    """Return Euler angles from quaternion for specified axis sequence.
-
-    >>> angles = euler_from_quaternion([0.06146124, 0, 0, 0.99810947])
-    >>> numpy.allclose(angles, [0.123, 0, 0])
-    True
-
-    """
-    return dcm2rpy(quat2dcm(quaternion), axes)
-    

svo_analysis/setup.py

@@ -0,0 +1,12 @@
+#!/usr/bin/env python
+
+from distutils.core import setup
+from catkin_pkg.python_setup import generate_distutils_setup
+
+d = generate_distutils_setup(
+    packages=['svo_analysis'],
+    package_dir={'': 'src'},
+    install_requires=['rospkg', 'yaml'],
+    )
+
+setup(**d)

svo_analysis/src/svo_analysis/analyse_dataset.py

@@ -4,7 +4,6 @@ import associate
 import numpy as np
 import matplotlib.pyplot as plt
 import yaml
-import transformations
 
 def loadDataset(filename):
   file = open(filename)

svo_analysis/src/svo_analysis/analyse_logs.py

@@ -0,0 +1,68 @@
+#!/usr/bin/python
+
+import os
+import numpy as np
+import matplotlib.pyplot as plt
+  
+def analyse_logs(D, trace_dir):
+
+  # identify measurements which result from normal frames and which from keyframes
+  is_kf = np.argwhere( (D['dropout'] == 1) & (D['repr_n_mps'] >= 0))
+  is_frame = np.argwhere(D['repr_n_mps'] >= 0)
+  is_nokf = np.argwhere( (D['dropout'] == 0) & (D['repr_n_mps'] >= 0))
+  n_frames = len(is_frame)
+  n_kfs = len(is_kf)
+  n_nokf = len(is_nokf)
+  
+  # set initial time to zero
+  D['timestamp'] = D['timestamp'] - D['timestamp'][0]
+  
+  # ----------------------------------------------------------------------------
+  # plot number of reprojected points
+  mean_n_reproj_points = np.mean(D['repr_n_mps'][is_frame]);
+  mean_n_reproj_matches = np.mean(D['repr_n_new_references'][is_frame]);
+  mean_n_edges_final = np.mean(D['sfba_n_edges_final'][is_frame]);
+
+  fig = plt.figure(figsize=(8,3))
+  ax = fig.add_subplot(111, xlabel='time [s]')
+  ax.plot(D['timestamp'][is_frame], D['repr_n_mps'][is_frame], 'r-',
+          label='Reprojected Points, avg = %.2f'%mean_n_reproj_points)
+  ax.plot(D['timestamp'][is_frame], D['repr_n_new_references'][is_frame], 'b-', 
+          label='Feature Matches, avg = %.2f'%mean_n_reproj_matches)
+  ax.plot(D['timestamp'][is_frame], D['sfba_n_edges_final'][is_frame], 'g-', 
+          label='Points after Optimization, avg = %.2f'%mean_n_edges_final)
+  ax.set_ylim(bottom=0)
+  ax.legend(loc='lower right')
+  fig.tight_layout()
+  fig.savefig(os.path.join(trace_dir,'num_reprojected.pdf'), bbox_inches="tight")
+
+  # ----------------------------------------------------------------------------
+  # plot median error before and after pose-optimzation and bundle adjustment
+  init_error_avg = np.mean(D['sfba_error_init'][is_frame])
+  opt1_avg = np.mean(D['sfba_error_final'][is_frame])
+
+  fig = plt.figure(figsize=(8,2))
+  ax = fig.add_subplot(111, xlabel='time [s]', ylabel='error [px]')
+  ax.plot(D['timestamp'][is_frame], D['sfba_error_init'][is_frame], 'r-', label='Initial error')
+  ax.plot(D['timestamp'][is_frame], D['sfba_error_final'][is_frame], 'b-', label='Final error')
+  ax.legend(ncol=2)  
+  fig.tight_layout()
+  fig.savefig(os.path.join(trace_dir,'reprojection_error.pdf'), bbox_inches="tight")
+  print 'average reprojection error improvement: ' + str(init_error_avg - opt1_avg)
+
+  # ----------------------------------------------------------------------------
+  # plot number of candidate points
+  fig = plt.figure(figsize=(8,3))
+  ax = fig.add_subplot(111, xlabel='time [s]')
+  ax.plot(D['timestamp'][is_frame], D['n_candidates'][is_frame], 'r-', label='Candidate Points')
+  fig.tight_layout()
+  fig.savefig(os.path.join(trace_dir,'candidate_points.pdf'), bbox_inches="tight")
+  
+  # ----------------------------------------------------------------------------
+  # plot number of candidate points
+  fig = plt.figure(figsize=(8,2))
+  ax = fig.add_subplot(111, xlabel='time [s]', ylabel='px')
+  ax.plot(D['timestamp'][is_frame], D['sfba_thresh'][is_frame], 'r-', label='Threshold')
+  fig.tight_layout()
+  fig.savefig(os.path.join(trace_dir,'optimization_thresh.pdf'), bbox_inches="tight")
+

svo_analysis/src/svo_analysis/analyse_timing.py

@@ -0,0 +1,68 @@
+#!/usr/bin/python
+
+import os
+import numpy as np
+import matplotlib.pyplot as plt
+
+def analyse_timing(D, trace_dir):
+
+  # identify measurements which result from normal frames and which from keyframes
+  is_frame = np.argwhere(D['repr_n_mps'] >= 0)
+  n_frames = len(is_frame)
+  
+  # set initial time to zero
+  D['timestamp'] = D['timestamp'] - D['timestamp'][0]
+
+  # ----------------------------------------------------------------------------
+  # plot total time for frame processing
+  avg_time = np.mean(D['tot_time'][is_frame])*1000;
+  fig = plt.figure(figsize=(8, 3))
+  ax = fig.add_subplot(111, ylabel='processing time [ms]', xlabel='time [s]')
+  ax.plot(D['timestamp'][is_frame], D['tot_time'][is_frame]*1000, 'g-', label='total time [ms]')
+  ax.plot(D['timestamp'][is_frame], np.ones(n_frames)*avg_time, 'b--', label=str('%(time).1fms mean time' % {'time': avg_time}))
+  ax.legend()
+  fig.tight_layout()
+  fig.savefig(os.path.join(trace_dir,'timing.pdf'), bbox_inches="tight")
+  
+  # ----------------------------------------------------------------------------
+  # plot boxplot
+  fig = plt.figure(figsize=(6,2))
+  ax = fig.add_subplot(111, xlabel='Processing time [ms]')
+  ax.boxplot([ 
+                D['tot_time'][is_frame]*1000,
+#                D['t_local_ba'][is_kf]*1000,
+                D['pose_optimizer'][is_frame]*1000 + D['point_optimizer'][is_frame]*1000,
+                D['reproject'][is_frame]*1000,
+                D['sparse_img_align'][is_frame]*1000,
+                D['pyramid_creation'][is_frame]*1000
+              ], 0,'', vert=0) 
+               
+  boxplot_labels = [
+                    r'\textbf{Total Motion Estimation: %2.2fms}' % np.median(D['tot_time'][is_frame]*1000),
+#                    'Local BA (KF only): %.2fms ' % np.median(D['local_ba'][is_kf]*1000),
+                    'Refinement: %2.2fms' % np.median(D['pose_optimizer'][is_frame]*1000 + D['point_optimizer'][is_frame]*1000),
+                    'Feature Alignment: %2.2fms' % np.median(D['reproject'][is_frame]*1000),
+                    'Sparse Image Alignment: %2.2fms' % np.median(D['sparse_img_align'][is_frame]*1000),
+                    'Pyramid Creation: %2.2fms' % np.median(D['pyramid_creation'][is_frame]*1000) ]
+                   
+  ax.set_yticks(np.arange(len(boxplot_labels))+1)
+  ax.set_yticklabels(boxplot_labels)
+  fig.tight_layout()
+  fig.savefig(os.path.join(trace_dir,'timing_boxplot.pdf'), bbox_inches="tight")
+  
+  # ----------------------------------------------------------------------------
+  # plot boxplot reprojection
+  fig = plt.figure(figsize=(6,2))
+  ax = fig.add_subplot(111, xlabel='Processing time [ms]')
+  ax.boxplot([ D['reproject'][is_frame]*1000,
+               D['feature_align'][is_frame]*1000,
+               D['reproject_candidates'][is_frame]*1000,
+               D['reproject_kfs'][is_frame]*1000 ], 0, '', vert=0) 
+  boxplot_labels = [r'\textbf{Total Reprojection: %2.2fms}' % np.median(D['reproject'][is_frame]*1000),
+                     'Feature Alignment: %2.2fms' % np.median(D['feature_align'][is_frame]*1000),
+                     'Reproject Candidates: %2.2fms' % np.median(D['reproject_candidates'][is_frame]*1000),
+                     'Reproject Keyframes: %2.2fms' % np.median(D['reproject_kfs'][is_frame]*1000) ]             
+  ax.set_yticks(np.arange(len(boxplot_labels))+1)
+  ax.set_yticklabels(boxplot_labels)
+  fig.tight_layout()
+  fig.savefig(os.path.join(trace_dir,'timing_reprojection.pdf'), bbox_inches="tight")

svo_analysis/src/svo_analysis/analyse_trajectory.py

@@ -1,43 +1,52 @@
 # -*- coding: utf-8 -*-
 
-import associate
-import numpy as np
-import matplotlib.pyplot as plt
+import os
 import yaml
-import transformations
+from .tum_benchmark_tools import associate
+import matplotlib.pyplot as plt
+import vikit_py.transformations as transformations
+import vikit_py.align_trajectory as align_trajectory
+import numpy as np
+
+# plot options
 from matplotlib import rc
+rc('font',**{'family':'serif','serif':['Cardo']})
+rc('text', usetex=True)
 
-def alignSim3(model, data, n):
+def plot_translation_error(timestamps, translation_error, results_dir):
+  fig = plt.figure(figsize=(8, 2.5))
+  ax = fig.add_subplot(111, xlabel='time [s]', ylabel='position drift [mm]', xlim=[0,timestamps[-1]-timestamps[0]+4])
+  ax.plot(timestamps-timestamps[0], translation_error[:,0]*1000, 'r-', label='x')
+  ax.plot(timestamps-timestamps[0], translation_error[:,1]*1000, 'g-', label='y')
+  ax.plot(timestamps-timestamps[0], translation_error[:,2]*1000, 'b-', label='z')
+  ax.legend()
+  fig.tight_layout()
+  fig.savefig(results_dir+'/translation_error.pdf')
 
-  # select the first n datapoints and remove its mean so their center is zero
-  M = model[0:n,:]
-  D = data[0:n,:]
+def plot_rotation_error(timestamps, rotation_error, results_dir):
+  fig = plt.figure(figsize=(8, 2.5))
+  ax = fig.add_subplot(111, xlabel='time [s]', ylabel='orientation drift [rad]', xlim=[0,timestamps[-1]-timestamps[0]+4])
+  ax.plot(timestamps-timestamps[0], rotation_error[:,0], 'r-', label='yaw')
+  ax.plot(timestamps-timestamps[0], rotation_error[:,1], 'g-', label='pitch')
+  ax.plot(timestamps-timestamps[0], rotation_error[:,2], 'b-', label='roll')
+  ax.legend()
+  fig.tight_layout()
+  fig.savefig(results_dir+'/orientation_error.pdf')
 
-  # substract mean
-  mu_M = M.mean(0)
-  mu_D = D.mean(0)
-  M_zerocentered = M - mu_M
-  D_zerocentered = D - mu_D
-
-  # correlation
-  C = 1.0/n*np.dot(M_zerocentered.transpose(), D_zerocentered)
-  sigma2 = 1.0/n*np.multiply(D_zerocentered,D_zerocentered).sum()
-  U_svd,D_svd,V_svd = np.linalg.linalg.svd(C)
-  D_svd = np.diag(D_svd)
-  V_svd = np.transpose(V_svd)
-  S = np.eye(3)
-
-  if(np.linalg.det(U_svd)*np.linalg.det(V_svd) < 0):
-    S[2,2] = -1
-
-  R = np.dot(U_svd, np.dot(S, np.transpose(V_svd)))
-  s = 1.0/sigma2*np.trace(np.dot(D_svd, S))
-  t = mu_M-s*np.dot(R,mu_D)
-
-  return s, R, t
-
-
-def getRigidBodyTrafo(quat,trans):
+def analyse_trajectory(results_dir):
+  # plot translation error
+  data = np.loadtxt(os.path.join(results_dir, 'translation_error.txt'))
+  timestamps = data[:,0]
+  translation_error = data[:,1:4]
+  plot_translation_error(timestamps, translation_error, results_dir)
+  
+  # plot orientation error
+  data = np.loadtxt(os.path.join(results_dir, 'orientation_error.txt'))
+  timestamps = data[:,0]
+  orientation_error = data[:,1:4]
+  plot_rotation_error(timestamps, orientation_error, results_dir)
+  
+def get_rigid_body_trafo(quat,trans):
   T = transformations.quaternion_matrix(quat)
   T[0:3,3] = trans
   return T
@@ -50,10 +59,6 @@ if __name__ == '__main__':
   save = True
   results_dir = '../results/'+dataset
   
-  # plot options
-  rc('font',**{'family':'serif','serif':['Cardo']})
-  rc('text', usetex=True)
-  
   # load dataset parameters
   params_stream = open(results_dir+'/params.yaml')
   params = yaml.load(params_stream)
@@ -72,7 +77,7 @@ if __name__ == '__main__':
   P = yaml.load(dataset_param_file_stream)
   T_cm_quat = np.array([P['calib_Tcm_qx'], P['calib_Tcm_qy'], P['calib_Tcm_qz'], P['calib_Tcm_qw']])
   T_cm_tran = np.array([P['calib_Tcm_tx'], P['calib_Tcm_ty'], P['calib_Tcm_tz']])
-  T_cm = getRigidBodyTrafo(T_cm_quat, T_cm_tran)
+  T_cm = get_rigid_body_trafo(T_cm_quat, T_cm_tran)
   T_mc = transformations.inverse_matrix(T_cm)
   
   # load data
@@ -94,12 +99,12 @@ if __name__ == '__main__':
   # align Sim3 to get scale
   if(n_align_frames > np.shape(matches)[0]):
     raise NameError('n_matches_for_scale is too large')
-  scale,rot,trans = alignSim3(p_gt, p_es, n_align_frames)
+  scale,rot,trans = align_trajectory.align_sim3(p_gt, p_es, n_align_frames)
   print 'scale = '+str(scale)
   
   # get trafo between (v)ision and (o)ptitrack frame
-  T_om = getRigidBodyTrafo(q_gt[0,:], p_gt[0,:])
-  T_vc = getRigidBodyTrafo(q_es[0,:], scale*p_es[0,:])
+  T_om = get_rigid_body_trafo(q_gt[0,:], p_gt[0,:])
+  T_vc = get_rigid_body_trafo(q_es[0,:], scale*p_es[0,:])
   T_cv = transformations.inverse_matrix(T_vc)
   T_ov = np.dot(T_om, np.dot(T_mc, T_cv))
   print 'T_ov = ' + str(T_ov)
@@ -111,7 +116,7 @@ if __name__ == '__main__':
   
   # rotate trajectory
   for i in range(np.shape(matches)[0]):
-    T_vc = getRigidBodyTrafo(q_es[i,:],p_es[i,:])
+    T_vc = get_rigid_body_trafo(q_es[i,:],p_es[i,:])
     T_vc[0:3,3] *= scale
     T_om = np.dot(T_ov, np.dot(T_vc, T_cm))
     p_es_aligned[i,:] = T_om[0:3,3]
@@ -136,15 +141,9 @@ if __name__ == '__main__':
           
   # plot position error (drift)
   translation_error = (p_gt-p_es_aligned)
-  fig = plt.figure(figsize=(8, 2.5))
-  ax = fig.add_subplot(111, xlabel='time [s]', ylabel='position drift [m]', xlim=[0,t_es[-1]+4])
-  ax.plot(t_es, translation_error[:,0], 'r-', label='x')
-  ax.plot(t_es, translation_error[:,1], 'g-', label='y')
-  ax.plot(t_es, translation_error[:,2], 'b-', label='z')
-  fig.tight_layout()
+  plot_translation_error(t_es, translation_error, results_dir)
   
   if save:
-    fig.savefig(results_dir+'/translation_error.pdf')
     f = open(translation_error_filename,'w')
     for i in range(translation_error.shape[0]):
       f.write('%.7f %.5f %.5f %.5f\n'%(t_es[i], translation_error[i,0],  translation_error[i,1],  translation_error[i,2]))
@@ -152,16 +151,9 @@ if __name__ == '__main__':
     
   # plot orientation error (drift)
   orientation_error = (rpy_gt - rpy_es_aligned)
-  fig = plt.figure(figsize=(8, 2.5))
-  ax = fig.add_subplot(111, xlabel='time [s]', ylabel='orientation drift [rad]', xlim=[0,t_es[-1]+4])
-  ax.plot(t_es, orientation_error[:,0], 'r--', label='yaw')
-  ax.plot(t_es, orientation_error[:,1], 'g--', label='pitch')
-  ax.plot(t_es, orientation_error[:,2], 'b--', label='roll')
-  ax.legend()
-  fig.tight_layout()
-  
-  if save:
-    fig.savefig(results_dir+'/orientation_error.pdf')
+  plot_rotation_error(t_es, orientation_error, results_dir)
+    
+  if save:  
     f = open(orientation_error_filename,'w')
     for i in range(orientation_error.shape[0]):
       f.write('%.7f %.5f %.5f %.5f\n'%(t_es[i], orientation_error[i,0],  orientation_error[i,1],  orientation_error[i,2]))

svo_analysis/src/svo_analysis/hand_eye_calib.py

@@ -11,81 +11,6 @@ import associate
 import rotation_utils as ru
 import matplotlib.pyplot as plt
 
-def alignSim3(model, data, n):
-
-  # select the first n datapoints and remove its mean so their center is zero
-  M = model[0:n,:]
-  D = data[0:n,:]
-
-  # substract mean
-  mu_M = M.mean(0)
-  mu_D = D.mean(0)
-  M_zerocentered = M - mu_M
-  D_zerocentered = D - mu_D
-
-  # correlation
-  C = 1.0/n*np.dot(M_zerocentered.transpose(), D_zerocentered)
-  sigma2 = 1.0/n*np.multiply(D_zerocentered,D_zerocentered).sum()
-  U_svd,D_svd,V_svd = np.linalg.linalg.svd(C)
-  D_svd = np.diag(D_svd)
-  V_svd = np.transpose(V_svd)
-  S = np.eye(3)
-
-  if(np.linalg.det(U_svd)*np.linalg.det(V_svd) < 0):
-    S[2,2] = -1
-
-  R = np.dot(U_svd, np.dot(S, np.transpose(V_svd)))
-  s = 1.0/sigma2*np.trace(np.dot(D_svd, S))
-  t = mu_M-s*np.dot(R,mu_D)
-
-  return s, R, t
-
-def matrixLog(A):
-  theta = np.arccos((np.trace(A)-1.0)/2.0)
-  log_theta = 0.5*theta/np.sin(theta) * (A - A.transpose())
-  x = np.array([log_theta[2,1], log_theta[0,2], log_theta[1,0]])
-  return x
-
-def handEyeCalib(q_gt, q_es, p_gt, p_es, I, delta=10, verbose=True):
-  ''' Implementation of the least squares solution described in the paper:
-  Robot Sensor Calibration: Solving AX=XB on the Euclidean Group
-  by Frank C. Park and Bryan J. Martin
-  '''
-  n = np.shape(I)[0]
-  M = np.zeros([3,3])
-  C = np.zeros([3*n, 3])
-  b_A = np.zeros([3*n,1])
-  b_B = np.zeros([3*n,1])
-  for ix, i in enumerate(I):
-    A1 = ru.quat2dcm(q_es[i,:])
-    A2 = ru.quat2dcm(q_es[i+delta,:])
-    A  = np.dot(A1.transpose(), A2)
-    B1 = ru.quat2dcm(q_gt[i,:])
-    B2 = ru.quat2dcm(q_gt[i+delta,:])
-    B  = np.dot(B1.transpose(), B2)
-    alpha = matrixLog(A)
-    beta = matrixLog(B)
-    M = M + np.dot(np.matrix(beta).transpose(), np.matrix(alpha))
-    C[3*ix:3*ix+3,:] = np.eye(3) - A
-    b_A[3*ix:3*ix+3,0] = np.dot(np.transpose(A1), p_es[i+delta,:]-p_es[i,:])
-    b_B[3*ix:3*ix+3,0] = np.dot(np.transpose(B1), p_gt[i+delta,:]-p_gt[i,:])
-
-  # compute rotation  
-  D,V = np.linalg.linalg.eig(np.dot(M.transpose(), M))
-  Lambda = np.diag([np.sqrt(1.0/D[0]), np.sqrt(1.0/D[1]), np.sqrt(1.0/D[2])])
-  Vinv = np.linalg.linalg.inv(V)
-  X = np.dot(V, np.dot(Lambda, np.dot(Vinv, M.transpose())))
-
-  # compute translation
-  d = np.zeros([3*n,1])
-  for i in range(n):
-    d[3*i:3*i+3,:] = b_A[3*i:3*i+3,:] - np.dot(X, b_B[3*i:3*i+3,:])
-
-  b = np.dot(np.linalg.inv(np.dot(np.transpose(C),C)),  np.dot(np.transpose(C),d))
-
-  return np.array(X),b
-
-
 # user config
 display = True
 dataset = '20130906_2149_ptam_i7_asl2'
@@ -121,7 +46,7 @@ q_es = np.array([[float(value) for value in data_es[b][3:7]] for a,b in matches]
 
 # --------------------------------------------------------------------------------
 # align Sim3 to get scale
-scale,rot,trans = alignSim3(p_gt, p_es, np.shape(matches)[0])
+scale,rot,trans = align_sim3(p_gt, p_es, np.shape(matches)[0])
 p_es = scale*p_es
 #p_es = np.transpose(scale*np.dot(rot,np.transpose(p_es)))+trans
 
@@ -142,7 +67,7 @@ if display:
 
 # select random measurements
 I = np.array(rand(n_measurements,1)*(np.shape(matches)[0]-delta), dtype=int)[:,0]
-R,b = handEyeCalib(q_gt, q_es, p_gt, p_es, I, delta, True)
+R,b = hand_eye_calib(q_gt, q_es, p_gt, p_es, I, delta, True)
 
 print 'quat = ' + str(ru.dcm2quat(R))
 print 'b = ' + str(b)

svo_analysis/src/svo_analysis/tum_benchmark_tools/associate.py

@@ -0,0 +1,128 @@
+#!/usr/bin/python
+# Software License Agreement (BSD License)
+#
+# Copyright (c) 2013, Juergen Sturm, TUM
+# All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions
+# are met:
+#
+#  * Redistributions of source code must retain the above copyright
+#    notice, this list of conditions and the following disclaimer.
+#  * Redistributions in binary form must reproduce the above
+#    copyright notice, this list of conditions and the following
+#    disclaimer in the documentation and/or other materials provided
+#    with the distribution.
+#  * Neither the name of TUM nor the names of its
+#    contributors may be used to endorse or promote products derived
+#    from this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
+# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
+# COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
+# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
+# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
+# LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
+# ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+# POSSIBILITY OF SUCH DAMAGE.
+#
+# Requirements: 
+# sudo apt-get install python-argparse
+
+"""
+The Kinect provides the color and depth images in an un-synchronized way. This means that the set of time stamps from the color images do not intersect with those of the depth images. Therefore, we need some way of associating color images to depth images.
+
+For this purpose, you can use the ''associate.py'' script. It reads the time stamps from the rgb.txt file and the depth.txt file, and joins them by finding the best matches.
+"""
+
+import argparse
+import sys
+import os
+import numpy
+
+
+def read_file_list(filename):
+    """
+    Reads a trajectory from a text file. 
+    
+    File format:
+    The file format is "stamp d1 d2 d3 ...", where stamp denotes the time stamp (to be matched)
+    and "d1 d2 d3.." is arbitary data (e.g., a 3D position and 3D orientation) associated to this timestamp. 
+    
+    Input:
+    filename -- File name
+    
+    Output:
+    dict -- dictionary of (stamp,data) tuples
+    
+    """
+    file = open(filename)
+    data = file.read()
+    lines = data.replace(","," ").replace("\t"," ").split("\n") 
+    list = [[v.strip() for v in line.split(" ") if v.strip()!=""] for line in lines if len(line)>0 and line[0]!="#"]
+    list = [(float(l[0]),l[1:]) for l in list if len(l)>1]
+    return dict(list)
+
+def associate(first_list, second_list,offset,max_difference):
+    """
+    Associate two dictionaries of (stamp,data). As the time stamps never match exactly, we aim 
+    to find the closest match for every input tuple.
+    
+    Input:
+    first_list -- first dictionary of (stamp,data) tuples
+    second_list -- second dictionary of (stamp,data) tuples
+    offset -- time offset between both dictionaries (e.g., to model the delay between the sensors)
+    max_difference -- search radius for candidate generation
+
+    Output:
+    matches -- list of matched tuples ((stamp1,data1),(stamp2,data2))
+    
+    """
+    first_keys = first_list.keys()
+    second_keys = second_list.keys()
+    potential_matches = [(abs(a - (b + offset)), a, b) 
+                         for a in first_keys 
+                         for b in second_keys 
+                         if abs(a - (b + offset)) < max_difference]
+    potential_matches.sort()
+    matches = []
+    for diff, a, b in potential_matches:
+        if a in first_keys and b in second_keys:
+            first_keys.remove(a)
+            second_keys.remove(b)
+            matches.append((a, b))
+    
+    matches.sort()
+    return matches
+
+if __name__ == '__main__':
+    
+    # parse command line
+    parser = argparse.ArgumentParser(description='''
+    This script takes two data files with timestamps and associates them   
+    ''')
+    parser.add_argument('first_file', help='first text file (format: timestamp data)')
+    parser.add_argument('second_file', help='second text file (format: timestamp data)')
+    parser.add_argument('--first_only', help='only output associated lines from first file', action='store_true')
+    parser.add_argument('--offset', help='time offset added to the timestamps of the second file (default: 0.0)',default=0.0)
+    parser.add_argument('--max_difference', help='maximally allowed time difference for matching entries (default: 0.02)',default=0.02)
+    args = parser.parse_args()
+
+    first_list = read_file_list(args.first_file)
+    second_list = read_file_list(args.second_file)
+
+    matches = associate(first_list, second_list,float(args.offset),float(args.max_difference))    
+
+    if args.first_only:
+        for a,b in matches:
+            print("%f %s"%(a," ".join(first_list[a])))
+    else:
+        for a,b in matches:
+            print("%f %s %f %s"%(a," ".join(first_list[a]),b-float(args.offset)," ".join(second_list[b])))
+            
+        

svo_analysis/src/svo_analysis/tum_benchmark_tools/evaluate_ate.py

@@ -0,0 +1,195 @@
+#!/usr/bin/python
+# Software License Agreement (BSD License)
+#
+# Copyright (c) 2013, Juergen Sturm, TUM
+# All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions
+# are met:
+#
+#  * Redistributions of source code must retain the above copyright
+#    notice, this list of conditions and the following disclaimer.
+#  * Redistributions in binary form must reproduce the above
+#    copyright notice, this list of conditions and the following
+#    disclaimer in the documentation and/or other materials provided
+#    with the distribution.
+#  * Neither the name of TUM nor the names of its
+#    contributors may be used to endorse or promote products derived
+#    from this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
+# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
+# COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
+# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
+# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
+# LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
+# ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+# POSSIBILITY OF SUCH DAMAGE.
+#
+# Requirements: 
+# sudo apt-get install python-argparse
+
+"""
+This script computes the absolute trajectory error from the ground truth
+trajectory and the estimated trajectory.
+"""
+
+import sys
+import numpy
+import argparse
+import associate
+
+def align(model,data):
+    """Align two trajectories using the method of Horn (closed-form).
+    
+    Input:
+    model -- first trajectory (3xn)
+    data -- second trajectory (3xn)
+    
+    Output:
+    rot -- rotation matrix (3x3)
+    trans -- translation vector (3x1)
+    trans_error -- translational error per point (1xn)
+    
+    """
+    numpy.set_printoptions(precision=3,suppress=True)
+    model_zerocentered = model - model.mean(1)
+    data_zerocentered = data - data.mean(1)
+    
+    W = numpy.zeros( (3,3) )
+    for column in range(model.shape[1]):
+        W += numpy.outer(model_zerocentered[:,column],data_zerocentered[:,column])
+    U,d,Vh = numpy.linalg.linalg.svd(W.transpose())
+    S = numpy.matrix(numpy.identity( 3 ))
+    if(numpy.linalg.det(U) * numpy.linalg.det(Vh)<0):
+        S[2,2] = -1
+    rot = U*S*Vh
+    trans = data.mean(1) - rot * model.mean(1)
+    
+    model_aligned = rot * model + trans
+    alignment_error = model_aligned - data
+    
+    trans_error = numpy.sqrt(numpy.sum(numpy.multiply(alignment_error,alignment_error),0)).A[0]
+        
+    return rot,trans,trans_error
+
+def plot_traj(ax,stamps,traj,style,color,label):
+    """
+    Plot a trajectory using matplotlib. 
+    
+    Input:
+    ax -- the plot
+    stamps -- time stamps (1xn)
+    traj -- trajectory (3xn)
+    style -- line style
+    color -- line color
+    label -- plot legend
+    
+    """
+    stamps.sort()
+    interval = numpy.median([s-t for s,t in zip(stamps[1:],stamps[:-1])])
+    x = []
+    y = []
+    last = stamps[0]
+    for i in range(len(stamps)):
+        if stamps[i]-last < 2*interval:
+            x.append(traj[i][0])
+            y.append(traj[i][1])
+        elif len(x)>0:
+            ax.plot(x,y,style,color=color,label=label)
+            label=""
+            x=[]
+            y=[]
+        last= stamps[i]
+    if len(x)>0:
+        ax.plot(x,y,style,color=color,label=label)
+            
+
+if __name__=="__main__":
+    # parse command line
+    parser = argparse.ArgumentParser(description='''
+    This script computes the absolute trajectory error from the ground truth trajectory and the estimated trajectory. 
+    ''')
+    parser.add_argument('first_file', help='ground truth trajectory (format: timestamp tx ty tz qx qy qz qw)')
+    parser.add_argument('second_file', help='estimated trajectory (format: timestamp tx ty tz qx qy qz qw)')
+    parser.add_argument('--offset', help='time offset added to the timestamps of the second file (default: 0.0)',default=0.0)
+    parser.add_argument('--scale', help='scaling factor for the second trajectory (default: 1.0)',default=1.0)
+    parser.add_argument('--max_difference', help='maximally allowed time difference for matching entries (default: 0.02)',default=0.02)
+    parser.add_argument('--save', help='save aligned second trajectory to disk (format: stamp2 x2 y2 z2)')
+    parser.add_argument('--save_associations', help='save associated first and aligned second trajectory to disk (format: stamp1 x1 y1 z1 stamp2 x2 y2 z2)')
+    parser.add_argument('--plot', help='plot the first and the aligned second trajectory to an image (format: png)')
+    parser.add_argument('--verbose', help='print all evaluation data (otherwise, only the RMSE absolute translational error in meters after alignment will be printed)', action='store_true')
+    args = parser.parse_args()
+
+    first_list = associate.read_file_list(args.first_file)
+    second_list = associate.read_file_list(args.second_file)
+
+    matches = associate.associate(first_list, second_list,float(args.offset),float(args.max_difference))    
+    if len(matches)<2:
+        sys.exit("Couldn't find matching timestamp pairs between groundtruth and estimated trajectory! Did you choose the correct sequence?")
+
+
+    first_xyz = numpy.matrix([[float(value) for value in first_list[a][0:3]] for a,b in matches]).transpose()
+    second_xyz = numpy.matrix([[float(value)*float(args.scale) for value in second_list[b][0:3]] for a,b in matches]).transpose()
+    rot,trans,trans_error = align(second_xyz,first_xyz)
+    
+    second_xyz_aligned = rot * second_xyz + trans
+    
+    first_stamps = first_list.keys()
+    first_stamps.sort()
+    first_xyz_full = numpy.matrix([[float(value) for value in first_list[b][0:3]] for b in first_stamps]).transpose()
+    
+    second_stamps = second_list.keys()
+    second_stamps.sort()
+    second_xyz_full = numpy.matrix([[float(value)*float(args.scale) for value in second_list[b][0:3]] for b in second_stamps]).transpose()
+    second_xyz_full_aligned = rot * second_xyz_full + trans
+    
+    if args.verbose:
+        print "compared_pose_pairs %d pairs"%(len(trans_error))
+
+        print "absolute_translational_error.rmse %f m"%numpy.sqrt(numpy.dot(trans_error,trans_error) / len(trans_error))
+        print "absolute_translational_error.mean %f m"%numpy.mean(trans_error)
+        print "absolute_translational_error.median %f m"%numpy.median(trans_error)
+        print "absolute_translational_error.std %f m"%numpy.std(trans_error)
+        print "absolute_translational_error.min %f m"%numpy.min(trans_error)
+        print "absolute_translational_error.max %f m"%numpy.max(trans_error)
+    else:
+        print "%f"%numpy.sqrt(numpy.dot(trans_error,trans_error) / len(trans_error))
+        
+    if args.save_associations:
+        file = open(args.save_associations,"w")
+        file.write("\n".join(["%f %f %f %f %f %f %f %f"%(a,x1,y1,z1,b,x2,y2,z2) for (a,b),(x1,y1,z1),(x2,y2,z2) in zip(matches,first_xyz.transpose().A,second_xyz_aligned.transpose().A)]))
+        file.close()
+        
+    if args.save:
+        file = open(args.save,"w")
+        file.write("\n".join(["%f "%stamp+" ".join(["%f"%d for d in line]) for stamp,line in zip(second_stamps,second_xyz_full_aligned.transpose().A)]))
+        file.close()
+
+    if args.plot:
+        import matplotlib
+        matplotlib.use('Agg')
+        import matplotlib.pyplot as plt
+        import matplotlib.pylab as pylab
+        from matplotlib.patches import Ellipse
+        fig = plt.figure()
+        ax = fig.add_subplot(111)
+        plot_traj(ax,first_stamps,first_xyz_full.transpose().A,'-',"black","ground truth")
+        plot_traj(ax,second_stamps,second_xyz_full_aligned.transpose().A,'-',"blue","estimated")
+
+        label="difference"
+        for (a,b),(x1,y1,z1),(x2,y2,z2) in zip(matches,first_xyz.transpose().A,second_xyz_aligned.transpose().A):
+            ax.plot([x1,x2],[y1,y2],'-',color="red",label=label)
+            label=""
+            
+        ax.legend()
+            
+        ax.set_xlabel('x [m]')
+        ax.set_ylabel('y [m]')
+        plt.savefig(args.plot,dpi=90)
+        

svo_analysis/src/svo_analysis/tum_benchmark_tools/evaluate_rpe.py

@@ -1,25 +1,92 @@
 #!/usr/bin/python
+# Software License Agreement (BSD License)
+#
+# Copyright (c) 2013, Juergen Sturm, TUM
+# All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions
+# are met:
+#
+#  * Redistributions of source code must retain the above copyright
+#    notice, this list of conditions and the following disclaimer.
+#  * Redistributions in binary form must reproduce the above
+#    copyright notice, this list of conditions and the following
+#    disclaimer in the documentation and/or other materials provided
+#    with the distribution.
+#  * Neither the name of TUM nor the names of its
+#    contributors may be used to endorse or promote products derived
+#    from this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
+# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
+# COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
+# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
+# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
+# LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
+# ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+# POSSIBILITY OF SUCH DAMAGE.
+
+"""
+This script computes the relative pose error from the ground truth trajectory
+and the estimated trajectory.
+"""
 
-import yaml
 import argparse
 import random
 import numpy
 import sys
-import transformations
-import matplotlib
-import matplotlib.pyplot as plt
-import matplotlib.pylab as pylab
-from matplotlib import rc
+
+_EPS = numpy.finfo(float).eps * 4.0
 
 def transform44(l):
+    """
+    Generate a 4x4 homogeneous transformation matrix from a 3D point and unit quaternion.
+    
+    Input:
+    l -- tuple consisting of (stamp,tx,ty,tz,qx,qy,qz,qw) where
+         (tx,ty,tz) is the 3D position and (qx,qy,qz,qw) is the unit quaternion.
+         
+    Output:
+    matrix -- 4x4 homogeneous transformation matrix
+    """
     t = l[1:4]
-    q = l[4:8]
-    return numpy.dot(transformations.translation_matrix(t),transformations.quaternion_matrix(q))
+    q = numpy.array(l[4:8], dtype=numpy.float64, copy=True)
+    nq = numpy.dot(q, q)
+    if nq < _EPS:
+        return numpy.array((
+        (                1.0,                 0.0,                 0.0, t[0])
+        (                0.0,                 1.0,                 0.0, t[1])
+        (                0.0,                 0.0,                 1.0, t[2])
+        (                0.0,                 0.0,                 0.0, 1.0)
+        ), dtype=numpy.float64)
+    q *= numpy.sqrt(2.0 / nq)
+    q = numpy.outer(q, q)
+    return numpy.array((
+        (1.0-q[1, 1]-q[2, 2],     q[0, 1]-q[2, 3],     q[0, 2]+q[1, 3], t[0]),
+        (    q[0, 1]+q[2, 3], 1.0-q[0, 0]-q[2, 2],     q[1, 2]-q[0, 3], t[1]),
+        (    q[0, 2]-q[1, 3],     q[1, 2]+q[0, 3], 1.0-q[0, 0]-q[1, 1], t[2]),
+        (                0.0,                 0.0,                 0.0, 1.0)
+        ), dtype=numpy.float64)
 
 def read_trajectory(filename, matrix=True):
+    """
+    Read a trajectory from a text file. 
+    
+    Input:
+    filename -- file to be read
+    matrix -- convert poses to 4x4 matrices
+    
+    Output:
+    dictionary of stamped 3D poses
+    """
     file = open(filename)
     data = file.read()
-    lines = data.replace(","," ").replace("\t"," ").split("\n")
+    lines = data.replace(","," ").replace("\t"," ").split("\n") 
     list = [[float(v.strip()) for v in line.split(" ") if v.strip()!=""] for line in lines if len(line)>0 and line[0]!="#"]
     list_ok = []
     for i,l in enumerate(list):
@@ -27,7 +94,7 @@ def read_trajectory(filename, matrix=True):
             continue
         isnan = False
         for v in l:
-            if numpy.isnan(v):
+            if numpy.isnan(v): 
                 isnan = True
                 break
         if isnan:
@@ -41,6 +108,16 @@ def read_trajectory(filename, matrix=True):
     return traj
 
 def find_closest_index(L,t):
+    """
+    Find the index of the closest value in a list.
+    
+    Input:
+    L -- the list
+    t -- value to be found
+    
+    Output:
+    index of the closest element
+    """
     beginning = 0
     difference = abs(L[0] - t)
     best = 0
@@ -59,9 +136,22 @@ def find_closest_index(L,t):
     return best
 
 def ominus(a,b):
+    """
+    Compute the relative 3D transformation between a and b.
+    
+    Input:
+    a -- first pose (homogeneous 4x4 matrix)
+    b -- second pose (homogeneous 4x4 matrix)
+    
+    Output:
+    Relative 3D transformation from a to b.
+    """
     return numpy.dot(numpy.linalg.inv(a),b)
 
 def scale(a,scalar):
+    """
+    Scale the translational components of a 4x4 homogeneous matrix by a scale factor.
+    """
     return numpy.array(
         [[a[0,0], a[0,1], a[0,2], a[0,3]*scalar],
          [a[1,0], a[1,1], a[1,2], a[1,3]*scalar],
@@ -70,13 +160,22 @@ def scale(a,scalar):
                        )
 
 def compute_distance(transform):
+    """
+    Compute the distance of the translational component of a 4x4 homogeneous matrix.
+    """
     return numpy.linalg.norm(transform[0:3,3])
 
 def compute_angle(transform):
+    """
+    Compute the rotation angle from a 4x4 homogeneous matrix.
+    """
     # an invitation to 3-d vision, p 27
     return numpy.arccos( min(1,max(-1, (numpy.trace(transform[0:3,0:3]) - 1)/2) ))
 
 def distances_along_trajectory(traj):
+    """
+    Compute the translational distances along a trajectory. 
+    """
     keys = traj.keys()
     keys.sort()
     motion = [ominus(traj[keys[i+1]],traj[keys[i]]) for i in range(len(keys)-1)]
@@ -86,8 +185,11 @@ def distances_along_trajectory(traj):
         sum += compute_distance(t)
         distances.append(sum)
     return distances
-
+    
 def rotations_along_trajectory(traj,scale):
+    """
+    Compute the angular rotations along a trajectory. 
+    """
     keys = traj.keys()
     keys.sort()
     motion = [ominus(traj[keys[i+1]],traj[keys[i]]) for i in range(len(keys)-1)]
@@ -97,60 +199,36 @@ def rotations_along_trajectory(traj,scale):
         sum += compute_angle(t)*scale
         distances.append(sum)
     return distances
+    
 
-def interpolate_transformation(alpha,a,b):
-    qa,ta = transformations.quaternion_from_matrix(a),transformations.translation_from_matrix(a)
-    qb,tb = transformations.quaternion_from_matrix(b),transformations.translation_from_matrix(b)
-    qr = transformations.quaternion_slerp(qa,qb,alpha)
-    tr = (1-alpha) * ta + (alpha) * tb
-    r = numpy.dot(transformations.translation_matrix(tr),transformations.quaternion_matrix(qr))
-    return r
-
-def interpolate_and_resample(traj_gt,stamps_est,param_offset,gt_max_time_difference):
-    traj_new = {}
-
+def evaluate_trajectory(traj_gt,traj_est,param_max_pairs=10000,param_fixed_delta=False,param_delta=1.00,param_delta_unit="s",param_offset=0.00,param_scale=1.00):
+    """
+    Compute the relative pose error between two trajectories.
+    
+    Input:
+    traj_gt -- the first trajectory (ground truth)
+    traj_est -- the second trajectory (estimated trajectory)
+    param_max_pairs -- number of relative poses to be evaluated
+    param_fixed_delta -- false: evaluate over all possible pairs
+                         true: only evaluate over pairs with a given distance (delta)
+    param_delta -- distance between the evaluated pairs
+    param_delta_unit -- unit for comparison:
+                        "s": seconds
+                        "m": meters
+                        "rad": radians
+                        "deg": degrees
+                        "f": frames
+    param_offset -- time offset between two trajectories (to model the delay)
+    param_scale -- scale to be applied to the second trajectory
+    
+    Output:
+    list of compared poses and the resulting translation and rotation error
+    """
     stamps_gt = list(traj_gt.keys())
-    stamps_gt.sort()
-
-    for t_est in stamps_est:
-        t_new = t_est + param_offset
-        i_nearest = find_closest_index(stamps_gt,t_est + param_offset)
-        if stamps_gt[i_nearest] >= t_new and i_nearest>0:
-            i_low = i_nearest-1
-            i_high = i_nearest
-        else:
-            i_low = i_nearest
-            i_high = i_nearest+1
-        if i_low<0 or i_high>=len(stamps_gt):
-            continue
-
-        t_low = stamps_gt[i_low]
-        t_high = stamps_gt[i_high]
-
-        if(abs( t_new - (t_low + param_offset) ) > gt_max_time_difference or
-           abs( t_new - (t_high + param_offset) ) > gt_max_time_difference):
-            continue
-
-        alpha = (t_new - (t_low + param_offset)) / (t_high - t_low)
-        transform = interpolate_transformation(alpha,traj_gt[t_low],traj_gt[t_high])
-        traj_new[t_new] = transform
-    return traj_new
-
-
-def evaluate_trajectory(traj_gt,traj_est,param_max_pairs=10000,param_fixed_delta=False,param_delta=1.00,param_delta_unit="s",param_offset=0.00,param_scale=1.00,param_interpolation=True):
     stamps_est = list(traj_est.keys())
-    stamps_est.sort()
-    stamps_gt = list(traj_gt.keys())
     stamps_gt.sort()
-
-    gt_interval = numpy.median([s-t for s,t in zip(stamps_gt[1:],stamps_gt[:-1])])
-    gt_max_time_difference = 2*gt_interval
-
-    if param_interpolation:
-        traj_gt = interpolate_and_resample(traj_gt,stamps_est,param_offset,gt_max_time_difference)
-        stamps_gt = list(traj_gt.keys())
-        stamps_gt.sort()
-
+    stamps_est.sort()
+    
     stamps_est_return = []
     for t_est in stamps_est:
         t_gt = stamps_gt[find_closest_index(stamps_gt,t_est + param_offset)]
@@ -184,11 +262,14 @@ def evaluate_trajectory(traj_gt,traj_est,param_max_pairs=10000,param_fixed_delta
         pairs = []
         for i in range(len(traj_est)):
             j = find_closest_index(index_est,index_est[i] + param_delta)
-            if j!=len(traj_est)-1:
+            if j!=len(traj_est)-1: 
                 pairs.append((i,j))
         if(param_max_pairs!=0 and len(pairs)>param_max_pairs):
             pairs = random.sample(pairs,param_max_pairs)
-
+    
+    gt_interval = numpy.median([s-t for s,t in zip(stamps_gt[1:],stamps_gt[:-1])])
+    gt_max_time_difference = 2*gt_interval
+    
     result = []
     for i,j in pairs:
         stamp_est_0 = stamps_est[i]
@@ -196,32 +277,29 @@ def evaluate_trajectory(traj_gt,traj_est,param_max_pairs=10000,param_fixed_delta
 
         stamp_gt_0 = stamps_gt[ find_closest_index(stamps_gt,stamp_est_0 + param_offset) ]
         stamp_gt_1 = stamps_gt[ find_closest_index(stamps_gt,stamp_est_1 + param_offset) ]
-
+        
         if(abs(stamp_gt_0 - (stamp_est_0 + param_offset)) > gt_max_time_difference  or
            abs(stamp_gt_1 - (stamp_est_1 + param_offset)) > gt_max_time_difference):
             continue
-
+        
         error44 = ominus(  scale(
                            ominus( traj_est[stamp_est_1], traj_est[stamp_est_0] ),param_scale),
                            ominus( traj_gt[stamp_gt_1], traj_gt[stamp_gt_0] ) )
-
         
         trans = compute_distance(error44)
         rot = compute_angle(error44)
-
-        motion_gt = ominus( traj_gt[stamp_gt_1], traj_gt[stamp_gt_0] )
-        trans_gt = compute_distance(motion_gt)
-        rot_gt = compute_angle(motion_gt)
         
-        if(rot < 0.07):
-          result.append([stamp_est_0,stamp_est_1,stamp_gt_0,stamp_gt_1,trans,rot,trans_gt,rot_gt])
-
+        result.append([stamp_est_0,stamp_est_1,stamp_gt_0,stamp_gt_1,trans,rot])
+        
     if len(result)<2:
         raise Exception("Couldn't find matching timestamp pairs between groundtruth and estimated trajectory!")
-
+        
     return result
 
 def percentile(seq,q):
+    """
+    Return the q-percentile of a list
+    """
     seq_sorted = list(seq)
     seq_sorted.sort()
     return seq_sorted[int((len(seq_sorted)-1)*q)]
@@ -229,45 +307,28 @@ def percentile(seq,q):
 if __name__ == '__main__':
     random.seed(0)
 
-    # config -------------------------------------------------------------------------
-    
-    dataset='20131003_1937_nslam_i7_asl2_fast'
-    dataset_dir = '../results/'+dataset
-    
-    #---------------------------------------------------------------------------------
-    
-    # load dataset parameters
-    params_stream = open(dataset_dir+'/params.yaml')
-    params = yaml.load(params_stream)
-    
-    # set trajectory groundtruth and estimate file
-    traj_groundtruth = params['dataset_directory']+'/groundtruth_filtered.txt'
-    traj_estimate_rotated = dataset_dir+'/traj_estimate_rotated.txt'
-
     parser = argparse.ArgumentParser(description='''
-    This script computes the relative pose error from the ground truth trajectory and the estimated trajectory.
+    This script computes the relative pose error from the ground truth trajectory and the estimated trajectory. 
     ''')
-    parser.add_argument('--groundtruth_file', help='ground-truth trajectory file (format: "timestamp tx ty tz qx qy qz qw")', default=traj_groundtruth)
-    parser.add_argument('--estimated_file', help='estimated trajectory file (format: "timestamp tx ty tz qx qy qz qw")', default=traj_estimate_rotated)
-    parser.add_argument('--interpolate', help='interpolate and resample the ground truth trajectory', default=True)
+    parser.add_argument('groundtruth_file', help='ground-truth trajectory file (format: "timestamp tx ty tz qx qy qz qw")')
+    parser.add_argument('estimated_file', help='estimated trajectory file (format: "timestamp tx ty tz qx qy qz qw")')
     parser.add_argument('--max_pairs', help='maximum number of pose comparisons (default: 10000, set to zero to disable downsampling)', default=10000)
-    parser.add_argument('--fixed_delta', help='only consider pose pairs that have a distance of delta delta_unit (e.g., for evaluating the drift per second/meter/radian)', default=True)
+    parser.add_argument('--fixed_delta', help='only consider pose pairs that have a distance of delta delta_unit (e.g., for evaluating the drift per second/meter/radian)', action='store_true')
     parser.add_argument('--delta', help='delta for evaluation (default: 1.0)',default=1.0)
     parser.add_argument('--delta_unit', help='unit of delta (options: \'s\' for seconds, \'m\' for meters, \'rad\' for radians, \'f\' for frames; default: \'s\')',default='s')
     parser.add_argument('--offset', help='time offset between ground-truth and estimated trajectory (default: 0.0)',default=0.0)
     parser.add_argument('--scale', help='scaling factor for the estimated trajectory (default: 1.0)',default=1.0)
     parser.add_argument('--save', help='text file to which the evaluation will be saved (format: stamp_est0 stamp_est1 stamp_gt0 stamp_gt1 trans_error rot_error)')
-    parser.add_argument('--plot', help='plot the result to a file (requires --fixed_delta, output format: png)', default=True)
-    parser.add_argument('--plotname', help='plot the result to a file (requires --fixed_delta, output format: png)', default='rpe.png')
-    parser.add_argument('--verbose', help='print all evaluation data (otherwise, only the mean translational error measured in meters will be printed)', default=True)
+    parser.add_argument('--plot', help='plot the result to a file (requires --fixed_delta, output format: png)')
+    parser.add_argument('--verbose', help='print all evaluation data (otherwise, only the mean translational error measured in meters will be printed)', action='store_true')
     args = parser.parse_args()
-
+    
     if args.plot and not args.fixed_delta:
         sys.exit("The '--plot' option can only be used in combination with '--fixed_delta'")
-
+    
     traj_gt = read_trajectory(args.groundtruth_file)
     traj_est = read_trajectory(args.estimated_file)
-
+    
     result = evaluate_trajectory(traj_gt,
                                  traj_est,
                                  int(args.max_pairs),
@@ -275,81 +336,47 @@ if __name__ == '__main__':
                                  float(args.delta),
                                  args.delta_unit,
                                  float(args.offset),
-                                 float(args.scale),
-                                 args.interpolate)
-
+                                 float(args.scale))
+    
     stamps = numpy.array(result)[:,0]
     trans_error = numpy.array(result)[:,4]
     rot_error = numpy.array(result)[:,5]
     
-    
-    results = dict()
-    results['trans_rmse'] = float(numpy.sqrt(numpy.dot(trans_error,trans_error) / len(trans_error)))
-    results['trans_mean'] = float(numpy.mean(trans_error))
-    results['trans_median'] = float(numpy.median(trans_error))
-    results['trans_std'] = float(numpy.std(trans_error))
-    results['trans_min'] = float(numpy.min(trans_error))
-    results['trans_max'] = float(numpy.max(trans_error))
-    results['rot_rmse'] = float((numpy.sqrt(numpy.dot(rot_error,rot_error) / len(rot_error)) * 180.0 / numpy.pi))
-    results['rot_mean'] = float((numpy.mean(rot_error) * 180.0 / numpy.pi))
-    results['rot_median'] = float(numpy.median(rot_error) * 180.0 / numpy.pi)
-    results['rot_std'] = float((numpy.std(rot_error) * 180.0 / numpy.pi))
-    results['rot_min'] = float((numpy.min(rot_error) * 180.0 / numpy.pi))
-    results['rot_max'] = float((numpy.max(rot_error) * 180.0 / numpy.pi))
-    
-    # dump experiment results to file:
-    results_file = params['trace_dir']+'/rpe_results.yaml'
-    with open(results_file,'w') as outfile:
-      outfile.write( yaml.dump(results, default_flow_style=False) )
-    
-    if args.save: 
+    if args.save:
         f = open(args.save,"w")
         f.write("\n".join([" ".join(["%f"%v for v in line]) for line in result]))
         f.close()
-
+    
     if args.verbose:
         print "compared_pose_pairs %d pairs"%(len(trans_error))
 
-        print "translational_error.rmse %f m"%results['trans_rmse']
-        print "translational_error.mean %f m"%results['trans_mean']
-        print "translational_error.median %f m"%results['trans_median']
-        print "translational_error.std %f m"%results['trans_std']
-        print "translational_error.min %f m"%results['trans_min']
-        print "translational_error.max %f m"%results['trans_max']
+        print "translational_error.rmse %f m"%numpy.sqrt(numpy.dot(trans_error,trans_error) / len(trans_error))
+        print "translational_error.mean %f m"%numpy.mean(trans_error)
+        print "translational_error.median %f m"%numpy.median(trans_error)
+        print "translational_error.std %f m"%numpy.std(trans_error)
+        print "translational_error.min %f m"%numpy.min(trans_error)
+        print "translational_error.max %f m"%numpy.max(trans_error)
 
-        print "rotational_error.rmse %f deg"%results['rot_rmse']
-        print "rotational_error.mean %f deg"%results['rot_mean']
-        print "rotational_error.median %f deg"%results['rot_median']
-        print "rotational_error.std %f deg"%results['rot_std']
-        print "rotational_error.min %f deg"%results['rot_min']
-        print "rotational_error.max %f deg"%results['rot_max']
+        print "rotational_error.rmse %f deg"%(numpy.sqrt(numpy.dot(rot_error,rot_error) / len(rot_error)) * 180.0 / numpy.pi)
+        print "rotational_error.mean %f deg"%(numpy.mean(rot_error) * 180.0 / numpy.pi)
+        print "rotational_error.median %f deg"%numpy.median(rot_error)
+        print "rotational_error.std %f deg"%(numpy.std(rot_error) * 180.0 / numpy.pi)
+        print "rotational_error.min %f deg"%(numpy.min(rot_error) * 180.0 / numpy.pi)
+        print "rotational_error.max %f deg"%(numpy.max(rot_error) * 180.0 / numpy.pi)
     else:
         print numpy.mean(trans_error)
 
-    if args.plot:
-      
-        # tell matplotlib to use latex font
-        rc('font',**{'family':'serif','serif':['Cardo']})
-        rc('text', usetex=True)
-  
-        #matplotlib.use('Agg') # no window should appear?
-
-        fig = plt.figure(figsize=(8, 3))
-        ax = fig.add_subplot(111)
+    if args.plot:    
+        import matplotlib
+        matplotlib.use('Agg')
+        import matplotlib.pyplot as plt
+        import matplotlib.pylab as pylab
+        fig = plt.figure()
+        ax = fig.add_subplot(111)        
         ax.plot(stamps - stamps[0],trans_error,'-',color="blue")
         #ax.plot([t for t,e in err_rot],[e for t,e in err_rot],'-',color="red")
         ax.set_xlabel('time [s]')
         ax.set_ylabel('translational error [m]')
-        plt.tight_layout()
-        plt.savefig(dataset_dir+'/rpe_trans_error.pdf')
-        plt.show()
+        plt.savefig(args.plot,dpi=300)
+        
 
-        fig = plt.figure(figsize=(8, 3))
-        ax = fig.add_subplot(111)
-        ax.plot(stamps - stamps[0],rot_error* 180.0 / numpy.pi,'-',color="blue")
-        #ax.plot([t for t,e in err_rot],[e for t,e in err_rot],'-',color="red")
-        ax.set_xlabel('time [s]')
-        ax.set_ylabel('rot error [deg]')
-        plt.tight_layout()
-        plt.savefig(dataset_dir+'/rpe_rot_error.pdf')
-        plt.show()

svo_ros/include/svo_ros/dataset_img.h

@@ -33,8 +33,8 @@ public:
   DatasetImg() {}
   virtual ~DatasetImg() {}
 
-  double stamp_;
-  std::string imgname_;
+  double timestamp_;
+  std::string image_name_;
 
   friend std::ostream& operator <<(std::ostream& out, const DatasetImg& pair);
   friend std::istream& operator >>(std::istream& in, DatasetImg& pair);
@@ -43,16 +43,16 @@ public:
 std::ostream& operator <<(std::ostream& out, const DatasetImg& gt)
 {
   out
-    << gt.stamp_ << " "
-    << gt.imgname_ << " "
+    << gt.timestamp_ << " "
+    << gt.image_name_ << " "
     << std::endl;
   return out;
 }
 
 std::istream& operator >>(std::istream& in, DatasetImg& gt)
 {
-  in >> gt.stamp_;
-  in >> gt.imgname_;
+  in >> gt.timestamp_;
+  in >> gt.image_name_;
   return in;
 }
 

svo_ros/src/benchmark_node.cpp

@@ -25,11 +25,14 @@
 #include <vikit/params_helper.h>
 #include <vikit/camera_loader.h>
 #include <vikit/abstract_camera.h>
-#include <vikit/user_input_thread.h>
+#include <vikit/blender_utils.h>
 #include <svo/config.h>
 #include <svo/frame.h>
+#include <svo/feature.h>
 #include <svo/map.h>
+#include <svo/point.h>
 #include <svo/frame_handler_mono.h>
+#include <svo/feature_detection.h>
 #include <svo_ros/visualizer.h>
 #include <svo_ros/dataset_img.h>
 
@@ -39,28 +42,35 @@ class BenchmarkNode
 {
   svo::FrameHandlerMono* vo_;
   svo::Visualizer visualizer_;
-  vk::UserInputThread user_input_thread_;
   int frame_count_;
   std::ofstream trace_est_pose_;
+  std::ofstream trace_trans_error_;
+  std::ofstream trace_rot_error_;
   vk::AbstractCamera* cam_;
-  bool quit_;
 
 public:
   BenchmarkNode(ros::NodeHandle& nh);
   ~BenchmarkNode();
-  void runCamImuBenchmark();
-  void processUserActions();
+  void tracePose(const SE3& T_w_f, const double timestamp);
+  void tracePoseError(const SE3& T_f_gt, const double timestamp);
+  void runBenchmark(const std::string& dataset_dir);
+  void runBlenderBenchmark(const std::string& dataset_dir);
 };
 
 BenchmarkNode::BenchmarkNode(ros::NodeHandle& nh) :
     vo_(NULL),
-    frame_count_(0),
-    quit_(false)
+    frame_count_(0)
 {
   // Create Camera
   if(!vk::camera_loader::loadFromRosNs("svo", cam_))
     throw std::runtime_error("Camera model not correctly specified.");
 
+  // create pose tracefile
+  std::string trace_est_name(Config::traceDir() + "/traj_estimate.txt");
+  trace_est_pose_.open(trace_est_name.c_str());
+  if(trace_est_pose_.fail())
+    throw std::runtime_error("Could not create tracefile. Does folder exist?");
+
   // Initialize VO
   vo_ = new svo::FrameHandlerMono(cam_);
   vo_->start();
@@ -72,71 +82,149 @@ BenchmarkNode::~BenchmarkNode()
   delete cam_;
 }
 
-void BenchmarkNode::runCamImuBenchmark()
+void BenchmarkNode::tracePose(const SE3& T_w_f, const double timestamp)
+{
+  Quaterniond q(T_w_f.unit_quaternion());
+  Vector3d p(T_w_f.translation());
+  trace_est_pose_.precision(15);
+  trace_est_pose_.setf(std::ios::fixed, std::ios::floatfield );
+  trace_est_pose_ << timestamp << " ";
+  trace_est_pose_.precision(6);
+  trace_est_pose_ << p.x() << " " << p.y() << " " << p.z() << " "
+                  << q.x() << " " << q.y() << " " << q.z() << " " << q.w() << endl;
+}
+
+void BenchmarkNode::tracePoseError(const SE3& T_f_gt, const double timestamp)
+{
+  Vector3d et(T_f_gt.translation()); // translation error
+  trace_trans_error_.precision(15);
+  trace_trans_error_.setf(std::ios::fixed, std::ios::floatfield );
+  trace_trans_error_ << timestamp << " ";
+  trace_trans_error_.precision(6);
+  trace_trans_error_ << et.x() << " " << et.y() << " " << et.z() << " " << endl;
+  Vector3d er(vk::dcm2rpy(T_f_gt.rotation_matrix())); // rotation error in roll-pitch-yaw
+  trace_rot_error_.precision(15);
+  trace_rot_error_.setf(std::ios::fixed, std::ios::floatfield );
+  trace_rot_error_ << timestamp << " ";
+  trace_rot_error_.precision(6);
+  trace_rot_error_ << er.x() << " " << er.y() << " " << er.z() << " " << endl;
+}
+
+void BenchmarkNode::runBenchmark(const std::string& dataset_dir)
 {
   // create image reader and load dataset
-  std::string benchmark_dir(vk::getParam<std::string>("svo/dataset_directory"));
-  std::string filename_benchmark(benchmark_dir + "/images.txt");
+  std::string filename_benchmark(dataset_dir + "/images.txt");
   vk::FileReader<FileType::DatasetImg> dataset_reader(filename_benchmark);
   dataset_reader.skipComments();
-  if(!dataset_reader.next())
-    std::runtime_error("Failed to open images file");
+  if(!dataset_reader.next()) {
+    SVO_ERROR_STREAM("Failed opening dataset: "<<filename_benchmark);
+    return;
+  }
   std::vector<FileType::DatasetImg> dataset;
   dataset_reader.readAllEntries(dataset);
 
-  // create pose tracefile
-  std::string trace_est_name(Config::traceDir() + "/traj_estimate.txt");
-  trace_est_pose_.open(trace_est_name.c_str());
-  if(trace_est_pose_.fail())
-    throw std::runtime_error("ERROR:frame_count_ could not create tracefile. probably the specified folder does not exist.");
-
   // process dataset
-  for(vk::vector<FileType::DatasetImg>::iterator it = dataset.begin();
-      it != dataset.end(); ++it, ++frame_count_)
+  for(auto it = dataset.begin(); it != dataset.end() && ros::ok(); ++it, ++frame_count_)
   {
     // Read image
-    std::string img_filename(benchmark_dir + "/" + it->imgname_);
+    std::string img_filename(dataset_dir + "/" + it->image_name_);
     cv::Mat img(cv::imread(img_filename, 0));
-    if(frame_count_ == 0)
-      SVO_INFO_STREAM("Reading image "<<img_filename);
-    if(img.empty())
-      throw std::runtime_error("Image could not be loaded.");
+    if(img.empty()) {
+      SVO_ERROR_STREAM("Reading image "<<img_filename<<" failed.");
+      return;
+    }
 
     // Add image to VO
-    processUserActions();
-    vo_->addImage(img, it->stamp_);
+    vo_->addImage(img, it->timestamp_);
 
     // Visualize
-    visualizer_.publishMinimal(img, vo_->lastFrame(), *vo_, it->stamp_);
-    if(vo_->stage() != svo::FrameHandlerMono::PAUSED)
-      visualizer_.visualizeMarkers(vo_->lastFrame(), vo_->coreKeyframes(), vo_->map());
+    visualizer_.publishMinimal(img, vo_->lastFrame(), *vo_, it->timestamp_);
 
     // write pose to tracefile
-    if(vo_->stage() == svo::FrameHandlerMono::DEFAULT_FRAME)
-    {
-      Quaterniond q(vo_->lastFrame()->T_f_w_.inverse().unit_quaternion());
-      Vector3d p(vo_->lastFrame()->T_f_w_.inverse().translation());
-      trace_est_pose_.precision(15);
-      trace_est_pose_.setf(std::ios::fixed, std::ios::floatfield );
-      trace_est_pose_ << it->stamp_<< " ";
-      trace_est_pose_.precision(6);
-      trace_est_pose_ << p.x() << " " << p.y() << " " << p.z() << " "
-                      << q.x() << " " << q.y() << " " << q.z() << " " << q.w() << endl;
-    }
-    if (!ros::ok() || quit_)
-      break;
+    if(vo_->stage() == svo::FrameHandlerMono::STAGE_DEFAULT_FRAME)
+      tracePose(vo_->lastFrame()->T_f_w_.inverse(), it->timestamp_);
   }
 }
 
-void BenchmarkNode::processUserActions()
+void BenchmarkNode::runBlenderBenchmark(const std::string& dataset_dir)
 {
-  char input = user_input_thread_.getInput();
-  switch(input)
+  // create image reader and load dataset
+  std::string filename_benchmark(dataset_dir + "/trajectory.txt");
+  vk::FileReader<vk::blender_utils::file_format::ImageNameAndPose> dataset_reader(filename_benchmark);
+  dataset_reader.skipComments();
+  if(!dataset_reader.next()) {
+    SVO_ERROR_STREAM("Failed opening dataset: "<<filename_benchmark);
+    return;
+  }
+  std::vector<vk::blender_utils::file_format::ImageNameAndPose> dataset;
+  dataset_reader.readAllEntries(dataset);
+
+  trace_trans_error_.open(Config::traceDir() + "/translation_error.txt");
+  trace_rot_error_.open(Config::traceDir() + "/orientation_error.txt");
+  if(trace_trans_error_.fail() || trace_rot_error_.fail())
+    throw std::runtime_error("Could not create tracefile. Does folder exist?");
+
+  // process dataset
+  for(auto it = dataset.begin(); it != dataset.end() && ros::ok(); ++it, ++frame_count_)
   {
-    case 'q': quit_ = true;   break;
-    case 'r': vo_->reset(); break;
-    case 's': vo_->start(); break;
-    default: ;
+    // Read image
+    std::string img_filename(dataset_dir + "/img/" + it->image_name_ + "_0.png");
+    cv::Mat img(cv::imread(img_filename, 0));
+    if(img.empty()) {
+      SVO_ERROR_STREAM("Reading image "<<img_filename<<" failed.");
+      return;
+    }
+
+    // Read ground-truth pose
+    Sophus::SE3 T_w_gt(it->q_, it->t_);
+
+    // Set reference frame with depth
+    if(frame_count_ == 0)
+    {
+      // set reference frame at ground-truth pose
+      FramePtr frame_ref(new Frame(cam_, img, it->timestamp_));
+      frame_ref->T_f_w_ = T_w_gt.inverse();
+
+      // load ground-truth depth
+      cv::Mat depthmap;
+      vk::blender_utils::loadBlenderDepthmap(
+          dataset_dir+"/depth/"+it->image_name_+"_0.depth", *cam_, depthmap);
+
+      // extract features, generate features with 3D points
+      svo::feature_detection::FastDetector detector;
+      svo::feature_detection::Corners corners;
+      detector.detect(frame_ref->img_pyr_, frame_ref->fts_, svo::Config::gridSize(),
+                      svo::Config::nPyrLevels(), svo::Config::triangMinCornerScore(), &corners);
+      for(auto corner=corners.begin(); corner!=corners.end(); ++corner)
+      {
+        if(corner->score < Config::triangMinCornerScore())
+          continue;
+        svo::Feature* ftr = new svo::Feature(frame_ref.get(), Eigen::Vector2d(corner->x, corner->y), corner->level);
+        Eigen::Vector3d pt_pos_cur = ftr->f*depthmap.at<float>(corner->y, corner->x);
+        Eigen::Vector3d pt_pos_world = frame_ref->T_f_w_.inverse()*pt_pos_cur;
+        svo::Point* point = new svo::Point(pt_pos_world, svo::Point::DEPTH);
+        ftr->point = point;
+        ftr->point->addFrameRef(ftr);
+        frame_ref->addFeature(ftr);
+      }
+      SVO_INFO_STREAM("Added "<<corners.size()<<" 3d pts to the reference frame.");
+      vo_->setFirstFrame(frame_ref);
+      SVO_INFO_STREAM("Set reference frame.");
+      continue;
+    }
+
+    // Add image to VO
+    vo_->addImage(img, it->timestamp_);
+
+    if(vo_->stage() != svo::FrameHandlerMono::STAGE_DEFAULT_FRAME)
+      continue;
+
+    // Compute pose error and trace to file
+    Sophus::SE3 T_f_gt(vo_->lastFrame()->T_f_w_*T_w_gt);
+    tracePoseError(T_f_gt, it->timestamp_);
+
+    // write pose to tracefile
+    tracePose(vo_->lastFrame()->T_f_w_.inverse(), it->timestamp_);
   }
 }
 
@@ -146,10 +234,12 @@ int main(int argc, char** argv)
 {
   ros::init(argc, argv, "svo");
   ros::NodeHandle nh;
-  {
-    svo::BenchmarkNode benchmark(nh);
-    benchmark.runCamImuBenchmark();
-  }
+  svo::BenchmarkNode benchmark(nh);
+  std::string benchmark_dir(vk::getParam<std::string>("svo/dataset_directory"));
+  if(vk::getParam<bool>("svo/dataset_is_blender", false))
+    benchmark.runBlenderBenchmark(benchmark_dir);
+  else
+    benchmark.runBenchmark(benchmark_dir);
   printf("BenchmarkNode finished.\n");
   return 0;
 }

svo_ros/src/visualizer.cpp

@@ -110,7 +110,7 @@ publishMinimal(const cv::Mat& img, const FramePtr& frame, const FrameHandlerMono
     cv::cvtColor(frame->img_pyr_[img_pub_level_], img_rgb, CV_GRAY2RGB);
 
     // during initialization, draw lines
-    if(slam.stage() == FrameHandlerBase::SECOND_FRAME)
+    if(slam.stage() == FrameHandlerBase::STAGE_SECOND_FRAME)
     {
       const vector<cv::Point2f>& px_ref(slam.initFeatureTrackRefPx());
       const vector<cv::Point2f>& px_cur(slam.initFeatureTrackCurPx());
@@ -148,7 +148,7 @@ publishMinimal(const cv::Mat& img, const FramePtr& frame, const FrameHandlerMono
     img_msg.encoding = sensor_msgs::image_encodings::BGR8;
     pub_images_.publish(img_msg.toImageMsg());
   }
-  else if(frame == NULL && pub_images_.getNumSubscribers() > 0 && slam.stage() == FrameHandlerBase::PAUSED)
+  else if(frame == NULL && pub_images_.getNumSubscribers() > 0 && slam.stage() == FrameHandlerBase::STAGE_PAUSED)
   {
     // display image when slam is not running
     cv_bridge::CvImage img_msg;

svo_ros/src/vo_node.cpp

@@ -110,13 +110,13 @@ imgCb(const sensor_msgs::ImageConstPtr& msg)
   vo_->addImage(img, msg->header.stamp.toSec());
   visualizer_.publishMinimal(img, vo_->lastFrame(), *vo_, msg->header.stamp.toSec());
 
-  if(publish_markers_ && vo_->stage() != FrameHandlerBase::PAUSED)
+  if(publish_markers_ && vo_->stage() != FrameHandlerBase::STAGE_PAUSED)
     visualizer_.visualizeMarkers(vo_->lastFrame(), vo_->coreKeyframes(), vo_->map());
 
   if(publish_dense_input_)
     visualizer_.exportToDense(vo_->lastFrame());
 
-  if(vo_->stage() == FrameHandlerMono::PAUSED)
+  if(vo_->stage() == FrameHandlerMono::STAGE_PAUSED)
     usleep(100000); // avoid busy loop when paused
 }