SVO has been tested under ROS Groovy and Hydro and Ubuntu 12.04 and 13.04. This is research code, expect that it changes often and any fitness for a particular purpose is disclaimed.
Only required if you want to run bundle adjustment. It is not necessary for visual odometry.
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
Sophus by Hauke Strasdat implements Lie groups that we need to describe rigid body transformations.
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.
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
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
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
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.
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.
Make sure to active the console window when pressing the keys.
sStart/RestartqQuitrReset
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.
You can generate a Doxygen documentation as follows
cd svo
doxygen Doxyfile
You are very welcome to contribute by opening a pull request via Github. I try to follow the ROS C++ style guide.
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.
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}
}