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SVO

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.

Installation Instructions

We use two workspaces, one for the plain CMake projects Sophus, Fast and optionally g2o and another workspace for the 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 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

OPTIONAL: g2o - General Graph Optimization

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 instance 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_slam.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

Open a new console and start SVO with the prepared launchfile:

roslaunch svo_ros test_rig3.launch

Open a new console and start RViz

rosrun rviz rviz

In RViz, load the configuration file (File > Open Config) which is stored in svo_ros/rviz_config.rviz.

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.

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.

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/slam_node.cpp.

Generating Code Documentation

You can generate a Doxygen documentation as follows

cd svo
doxygen Doxyfile

Contributing

You are very welcome to contribute by opening a pull request via Github. I try to follow the ROS C++ style guide.

Licence

The source code is released under 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.

Citing

If you use SVO in an academic context, please cite the following publication:

@article{Pomerleau12comp,
  author = {Forster, Christian and Pizzoli, Matia and Scaramuzza, Davide},
  title = {{SVO: Fast Semi-Direct Monocular Visual Odometry}},
  journal = {IEEE International Conference on Robotics and Automation (ICRA)},
  year = {2014}
}