SVO === This code implements a semi-direct visual odometry pipeline that is described in the paper * C. Forster, M. Pizzoli, D. Scaramuzza, "__SVO: Fast Semi-Direct Monocular Visual Odometry__," IEEE International Conference on Robotics and Automation (ICRA), 2014. Video: http://youtu.be/2YnIMfw6bJY Disclaimer ---------- SVO has been tested under ROS Groovy and Hydro and Ubuntu 12.04 and 13.04. This is research code, any fitness for a particular purpose is disclaimed. Licence ------- The source code is released under a GPLv3 licence. A professional edition license for closed-source projects is also available. Please contact `forster at ifi dot uzh dot ch` for further information. Documentation ------------- The API is documented here: http://uzh-rpg.github.io/rpg_svo/doc/ Installation Instructions ------------------------- SVO can be used conveniently with ROS (www.ros.org). We use two workspaces, one for the plain CMake projects `Sophus`, `Fast` and optionally `g2o` and another workspace for the ROS-Catkin projects `rpg_vikit` and `rpg_svo`. Make sure to clone in the right folder. #### Sophus - Lie groups Sophus by Hauke Strasdat implements Lie groups that we need to describe rigid body transformations. Checkout in your workspace for plain CMake projects. cd workspace git clone https://github.com/strasdat/Sophus.git cd Sophus git checkout a621ff mkdir build cd build cmake .. make You don't need to install the library since `cmake ..` writes the package location to `~.cmake/packages/` where CMake can later find it. #### Fast - Corner Detector The Fast detector by Edward Rosten is used to detect corners. To simplify installation we provide a CMake package that contains the fast detector from the libCVD library (http://www.edwardrosten.com/cvd/). cd workspace git clone https://github.com/uzh-rpg/fast.git cd fast mkdir build cd build cmake .. make #### g2o - General Graph Optimization _OPTIONAL_ Only required if you want to run bundle adjustment. It is not necessary for visual odometry. In fact, we don't run it on our MAVs. g2o requires the following system dependencies: `cmake, libeigen3-dev, libsuitesparse-dev, libqt4-dev, qt4-qmake, libqglviewer-qt4-dev`, install them with `apt-get` I suggest an out-of-source build of g2o: cd workspace git clone https://github.com/RainerKuemmerle/g2o.git cd g2o mkdir build cd build cmake .. make sudo make install If you don't want to make a system install, then you can replace the cmake command with `cmake .. -DCMAKE_INSTALL_PREFIX:PATH=$HOME/installdir` #### vikit - Some useful tools that we need vikit for contains camera models, some math and interpolation functions that SVO needs. vikit is a catkin project, therefore, download it into your catkin workspace source folder. cd catkin_ws/src git clone https://github.com/uzh-rpg/rpg_vikit.git #### SVO Now we are ready to build SVO. Clone it into your catkin workspace cd catkin_ws/src git clone https://github.com/uzh-rpg/rpg_svo.git If you installed g2o then set `HAVE_G2O` in `svo/CMakeLists.txt` to TRUE. Then build catkin_make Run SVO on a Dataset ------------------------- Download this example dataset: [rpg.ifi.uzh.ch/datasets/airground_rig_s3_2013-03-18_21-38-48.bag](http://rpg.ifi.uzh.ch/datasets/airground_rig_s3_2013-03-18_21-38-48.bag) Open a new console and start SVO with the prepared launchfile: roslaunch svo_ros test_rig3.launch Now you are ready to start the rosbag. Open a new console and change to the directory where you have downloaded the example dataset. Then type: rosbag play airground_rig_s3_2013-03-18_21-38-48.bag Now you should see the video with tracked features (green) and in RViz how the camera moves. If you want to see the number of tracked features, fps and tracking quality, run the GUI. SVO GUI ------- Type `rosrun rqt_svo rqt_svo` to run the SVO widget that displays the number of tracked features, the frame rate and provides some interface buttons. If the widget is not found, try this: rm ~/.config/ros.org/rqt_gui.ini rosrun rqt_svo rqt_svo Keyboard Shortcuts ------------------ Make sure to active the console window when pressing the keys. * `s` Start/Restart * `q` Quit * `r` Reset Camera Calibration ------------------ You need a calibrated camera to run SVO. We use the Matrix Vision Bluefox cameras in our lab, which have VGA resolution and global shutter. SVO supports three camera models: 1. The `ATAN` model - our preference - which is also used by _PTAM_. This model uses the _FOV_ distortion model of "Deverneay and Faugeras, Straight lines have to be straight, 2001". You can calibrate your camera with this model by using the calibration tool in this package: https://github.com/ethz-asl/ethzasl_ptam We prefer this model because the projection and unprojection can be computed faster than with the other models. Further, the industral cameras that we use have neglectable tangential distortion. 2. The `Pinhole` model with three radial and two tangential distortion parameters. This model is standard in OpenCV and ROS. You can use the ROS camera calibration tool: http://wiki.ros.org/camera_calibration 3. The `Ocam` model by Davide Scaramuzza which can be used to model cameras with high field of view or even omnidirectional cameras. Use the OCamCalib toolbox to calibrate your camera: http://rpg.ifi.uzh.ch/software_datasets.html Although SVO can be used with this camera model, the algorithm does not work yet with omnidirectional cameras. We save the calibration in _YAML_ format (see examples in `svo_ros/param`) and include it in the launchfiles (see examples in `svo_ros/launch`). Parameter Settings ------------------ A description of all parameters which can be set via the launchfile is provided in `svo/include/config.h`. The default parameters can be viewed in `svo/src/config.cpp`. Moreover, some additional parameters (mainly rostopic names etc.) are read from the ros parameter server in `svo_ros/vo_node.cpp`. Contributing ------------ You are very welcome to contribute to SVO by opening a pull request via Github. I try to follow the ROS C++ style guide http://wiki.ros.org/CppStyleGuide Citing ------ If you use SVO in an academic context, please cite the following publication: @inproceedings{Forster2014ICRA, author = {Forster, Christian and Pizzoli, Matia and Scaramuzza, Davide}, title = {{SVO}: Fast Semi-Direct Monocular Visual Odometry}, booktitle = {IEEE International Conference on Robotics and Automation (ICRA)}, year = {2014} }