275 lines
10 KiB
C++
275 lines
10 KiB
C++
// This file is part of SVO - Semi-direct Visual Odometry.
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//
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// Copyright (C) 2014 Christian Forster <forster at ifi dot uzh dot ch>
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// (Robotics and Perception Group, University of Zurich, Switzerland).
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//
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// SVO is free software: you can redistribute it and/or modify it under the
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// terms of the GNU General Public License as published by the Free Software
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// Foundation, either version 3 of the License, or any later version.
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//
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// SVO is distributed in the hope that it will be useful, but WITHOUT ANY
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// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
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// FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
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//
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// You should have received a copy of the GNU General Public License
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// along with this program. If not, see <http://www.gnu.org/licenses/>.
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#include <algorithm>
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#include <svo/sparse_img_align.h>
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#include <svo/frame.h>
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#include <svo/feature.h>
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#include <svo/config.h>
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#include <svo/point.h>
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#include <vikit/abstract_camera.h>
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#include <vikit/vision.h>
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#include <vikit/math_utils.h>
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namespace svo {
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SparseImgAlign::SparseImgAlign(
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int max_level, int min_level, int n_iter,
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Method method, bool display, bool verbose) :
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display_(display),
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max_level_(max_level),
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min_level_(min_level)
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{
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n_iter_ = n_iter;
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n_iter_init_ = n_iter_;
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method_ = method;
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verbose_ = verbose;
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eps_ = 0.000001;
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}
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size_t SparseImgAlign::run(FramePtr ref_frame, FramePtr cur_frame)
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{
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reset();
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if(ref_frame->fts_.empty())
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{
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SVO_WARN_STREAM("SparseImgAlign: no features to track!");
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return 0;
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}
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ref_frame_ = ref_frame;
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cur_frame_ = cur_frame;
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ref_patch_cache_ = cv::Mat(ref_frame_->fts_.size(), patch_area_, CV_32F);
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jacobian_cache_.resize(Eigen::NoChange, ref_patch_cache_.rows*patch_area_);
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visible_fts_.resize(ref_patch_cache_.rows, false); // TODO: should it be reset at each level?
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SE3 T_cur_from_ref(cur_frame_->T_f_w_ * ref_frame_->T_f_w_.inverse());
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for(level_=max_level_; level_>=min_level_; --level_)
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{
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mu_ = 0.1;
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jacobian_cache_.setZero();
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have_ref_patch_cache_ = false;
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if(verbose_)
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printf("\nPYRAMID LEVEL %i\n---------------\n", level_);
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optimize(T_cur_from_ref);
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}
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cur_frame_->T_f_w_ = T_cur_from_ref * ref_frame_->T_f_w_;
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return n_meas_/patch_area_;
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}
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Matrix<double, 6, 6> SparseImgAlign::getFisherInformation()
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{
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double sigma_i_sq = 5e-4*255*255; // image noise
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Matrix<double,6,6> I = H_/sigma_i_sq;
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return I;
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}
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void SparseImgAlign::precomputeReferencePatches()
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{
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const int border = patch_halfsize_+1;
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const cv::Mat& ref_img = ref_frame_->img_pyr_.at(level_);
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const int stride = ref_img.cols;
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const float scale = 1.0f/(1<<level_);
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const Vector3d ref_pos = ref_frame_->pos();
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const double focal_length = ref_frame_->cam_->errorMultiplier2();
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size_t feature_counter = 0;
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std::vector<bool>::iterator visiblity_it = visible_fts_.begin();
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for(auto it=ref_frame_->fts_.begin(), ite=ref_frame_->fts_.end();
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it!=ite; ++it, ++feature_counter, ++visiblity_it)
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{
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// check if reference with patch size is within image
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const float u_ref = (*it)->px[0]*scale;
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const float v_ref = (*it)->px[1]*scale;
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const int u_ref_i = floorf(u_ref);
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const int v_ref_i = floorf(v_ref);
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if((*it)->point == NULL || u_ref_i-border < 0 || v_ref_i-border < 0 || u_ref_i+border >= ref_img.cols || v_ref_i+border >= ref_img.rows)
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continue;
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*visiblity_it = true;
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// cannot just take the 3d points coordinate because of the reprojection errors in the reference image!!!
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const double depth(((*it)->point->pos_ - ref_pos).norm());
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const Vector3d xyz_ref((*it)->f*depth);
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// compute bilateral interpolation weights for reference image
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const float subpix_u_ref = u_ref-u_ref_i;
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const float subpix_v_ref = v_ref-v_ref_i;
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const float w_ref_tl = (1.0-subpix_u_ref) * (1.0-subpix_v_ref);
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const float w_ref_tr = subpix_u_ref * (1.0-subpix_v_ref);
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const float w_ref_bl = (1.0-subpix_u_ref) * subpix_v_ref;
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const float w_ref_br = subpix_u_ref * subpix_v_ref;
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size_t pixel_counter = 0;
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float* cache_ptr = reinterpret_cast<float*>(ref_patch_cache_.data) + patch_area_*feature_counter;
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for(int y=0; y<patch_size_; ++y)
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{
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uint8_t* ref_img_ptr = (uint8_t*) ref_img.data + (v_ref_i+y-patch_halfsize_)*stride + (u_ref_i-patch_halfsize_);
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for(int x=0; x<patch_size_; ++x, ++ref_img_ptr, ++cache_ptr, ++pixel_counter)
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{
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// precompute interpolated reference patch color
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*cache_ptr = w_ref_tl*ref_img_ptr[0] + w_ref_tr*ref_img_ptr[1] + w_ref_bl*ref_img_ptr[stride] + w_ref_br*ref_img_ptr[stride+1];
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// we use the inverse compositional: thereby we can take the gradient always at the same position
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// get gradient of warped image (~gradient at warped position)
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float dx = 0.5f * ((w_ref_tl*ref_img_ptr[1] + w_ref_tr*ref_img_ptr[2] + w_ref_bl*ref_img_ptr[stride+1] + w_ref_br*ref_img_ptr[stride+2])
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-(w_ref_tl*ref_img_ptr[-1] + w_ref_tr*ref_img_ptr[0] + w_ref_bl*ref_img_ptr[stride-1] + w_ref_br*ref_img_ptr[stride]));
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float dy = 0.5f * ((w_ref_tl*ref_img_ptr[stride] + w_ref_tr*ref_img_ptr[1+stride] + w_ref_bl*ref_img_ptr[stride*2] + w_ref_br*ref_img_ptr[stride*2+1])
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-(w_ref_tl*ref_img_ptr[-stride] + w_ref_tr*ref_img_ptr[1-stride] + w_ref_bl*ref_img_ptr[0] + w_ref_br*ref_img_ptr[1]));
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// evaluate jacobian and cache
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Matrix<double,2,6> frame_jac;
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Frame::jacobian_xyz2uv(xyz_ref, frame_jac);
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// cache the jacobian
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jacobian_cache_.col(feature_counter*patch_area_ + pixel_counter) =
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(dx*frame_jac.row(0) + dy*frame_jac.row(1))*(focal_length / (1<<level_));
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}
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}
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}
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have_ref_patch_cache_ = true;
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}
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double SparseImgAlign::computeResiduals(
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const SE3& T_cur_from_ref,
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bool linearize_system,
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bool compute_weight_scale)
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{
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// Warp the (cur)rent image such that it aligns with the (ref)erence image
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const cv::Mat& cur_img = cur_frame_->img_pyr_.at(level_);
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if(linearize_system && display_)
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resimg_ = cv::Mat(cur_img.size(), CV_32F, cv::Scalar(0));
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if(have_ref_patch_cache_ == false)
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precomputeReferencePatches();
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// compute the weights on the first iteration
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std::vector<float> errors;
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if(compute_weight_scale)
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errors.reserve(visible_fts_.size());
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const int stride = cur_img.cols;
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const int border = patch_halfsize_+1;
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const float scale = 1.0f/(1<<level_);
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const Vector3d ref_pos(ref_frame_->pos());
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float chi2 = 0.0;
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size_t feature_counter = 0; // is used to compute the index of the cached jacobian
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std::vector<bool>::iterator visiblity_it = visible_fts_.begin();
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for(auto it=ref_frame_->fts_.begin(); it!=ref_frame_->fts_.end();
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++it, ++feature_counter, ++visiblity_it)
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{
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// check if feature is within image
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if(!*visiblity_it)
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continue;
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// compute pixel location in cur img
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const double depth = ((*it)->point->pos_ - ref_pos).norm();
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const Vector3d xyz_ref((*it)->f*depth);
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const Vector3d xyz_cur(T_cur_from_ref * xyz_ref);
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const Vector2f uv_cur_pyr(cur_frame_->cam_->world2cam(xyz_cur).cast<float>() * scale);
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const float u_cur = uv_cur_pyr[0];
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const float v_cur = uv_cur_pyr[1];
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const int u_cur_i = floorf(u_cur);
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const int v_cur_i = floorf(v_cur);
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// check if projection is within the image
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if(u_cur_i < 0 || v_cur_i < 0 || u_cur_i-border < 0 || v_cur_i-border < 0 || u_cur_i+border >= cur_img.cols || v_cur_i+border >= cur_img.rows)
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continue;
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// compute bilateral interpolation weights for the current image
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const float subpix_u_cur = u_cur-u_cur_i;
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const float subpix_v_cur = v_cur-v_cur_i;
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const float w_cur_tl = (1.0-subpix_u_cur) * (1.0-subpix_v_cur);
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const float w_cur_tr = subpix_u_cur * (1.0-subpix_v_cur);
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const float w_cur_bl = (1.0-subpix_u_cur) * subpix_v_cur;
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const float w_cur_br = subpix_u_cur * subpix_v_cur;
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float* ref_patch_cache_ptr = reinterpret_cast<float*>(ref_patch_cache_.data) + patch_area_*feature_counter;
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size_t pixel_counter = 0; // is used to compute the index of the cached jacobian
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for(int y=0; y<patch_size_; ++y)
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{
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uint8_t* cur_img_ptr = (uint8_t*) cur_img.data + (v_cur_i+y-patch_halfsize_)*stride + (u_cur_i-patch_halfsize_);
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for(int x=0; x<patch_size_; ++x, ++pixel_counter, ++cur_img_ptr, ++ref_patch_cache_ptr)
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{
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// compute residual
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const float intensity_cur = w_cur_tl*cur_img_ptr[0] + w_cur_tr*cur_img_ptr[1] + w_cur_bl*cur_img_ptr[stride] + w_cur_br*cur_img_ptr[stride+1];
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const float res = intensity_cur - (*ref_patch_cache_ptr);
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// used to compute scale for robust cost
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if(compute_weight_scale)
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errors.push_back(fabsf(res));
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// robustification
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float weight = 1.0;
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if(use_weights_) {
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weight = weight_function_->value(res/scale_);
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}
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chi2 += res*res*weight;
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n_meas_++;
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if(linearize_system)
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{
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// compute Jacobian, weighted Hessian and weighted "steepest descend images" (times error)
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const Vector6d J(jacobian_cache_.col(feature_counter*patch_area_ + pixel_counter));
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H_.noalias() += J*J.transpose()*weight;
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Jres_.noalias() -= J*res*weight;
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if(display_)
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resimg_.at<float>((int) v_cur+y-patch_halfsize_, (int) u_cur+x-patch_halfsize_) = res/255.0;
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}
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}
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}
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}
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// compute the weights on the first iteration
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if(compute_weight_scale && iter_ == 0)
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scale_ = scale_estimator_->compute(errors);
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return chi2/n_meas_;
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}
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int SparseImgAlign::solve()
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{
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x_ = H_.ldlt().solve(Jres_);
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if((bool) std::isnan((double) x_[0]))
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return 0;
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return 1;
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}
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void SparseImgAlign::update(
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const ModelType& T_curold_from_ref,
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ModelType& T_curnew_from_ref)
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{
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T_curnew_from_ref = T_curold_from_ref * SE3::exp(-x_);
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}
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void SparseImgAlign::startIteration()
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{}
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void SparseImgAlign::finishIteration()
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{
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if(display_)
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{
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cv::namedWindow("residuals", CV_WINDOW_AUTOSIZE);
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cv::imshow("residuals", resimg_*10);
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cv::waitKey(0);
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}
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}
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} // namespace svo
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