Installation Using ROS

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.

If make fails with the error lvalue required as left operand of assignment in so2.cpp, replace the problematic lines in the so2.cpp constructor:

unit_complex_.real() = 1.;
unit_complex_.imag() = 0.;

with the corrected line:

unit_complex_ = std::complex<double>(1.0, 0.0);

After making these changes, try rebuilding the project.

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.

Install g2o from source:

git clone https://github.com/RainerKuemmerle/g2o.git
cd g2o
mkdir build
cd build
cmake ..
make
sudo make install

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

Also python em(empy) module is required:

pip install empy

You need to update outdated OpenCV constants that are not recognized in newer versions of OpenCV.

1. Replace CV_INTER_LINEAR:
   • Find and replace all instances of CV_INTER_LINEAR with cv::INTER_LINEAR in your code.

     sed -i 's/CV_INTER_LINEAR/cv::INTER_LINEAR/g' $(find ./catkin_ws/src -type f -name '*.cpp')
     

2. Replace CV_RANSAC:
   • Find and replace all instances of CV_RANSAC with cv::RANSAC in your code.

     sed -i 's/CV_RANSAC/cv::RANSAC/g' $(find ./catkin_ws/src -type f -name '*.cpp')
     

3. Replace CV_WINDOW_AUTOSIZE:
   • Find and replace all instances of CV_WINDOW_AUTOSIZE with cv::WINDOW_AUTOSIZE in your code.

     sed -i 's/CV_WINDOW_AUTOSIZE/cv::WINDOW_AUTOSIZE/g' $(find ./catkin_ws/src -type f -name '*.cpp')
     

ROS dependencies

Sometimes cmake-modules is missing (required for including Eigen in ROS Indigo):

sudo apt-get install ros-hydro-cmake-modules

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. If you installed g2o in $HOME/installdir, you can tell find_package by setting the environment variable G2O_ROOT

export G2O_ROOT=$HOME/installdir

Once again you need to update outdated OpenCV constants that are not recognized in newer versions of OpenCV.

1. CV_GRAY2RGB is replaced with cv::COLOR_GRAY2RGB.

   find ./ -type f -exec sed -i 's/CV_GRAY2RGB/cv::COLOR_GRAY2RGB/g' {} +
   

2. CV_FILLED is replaced with cv::FILLED.

   find ./ -type f -exec sed -i 's/CV_FILLED/cv::FILLED/g' {} +
   

Finally, build and source the setup.bash:

catkin_make
source devel/setup.bash

Run Using ROS

Run SVO on a Dataset

First you have to save a video stream into a bag file in ROS. In order to do it, you need to record the relevant topics that include the video data. Here are the steps to achieve this:

1. Identify the Topic: Determine the topic name that publishes the video data, such as /camera/image_raw.
2. Use rosbag to Record: Use the rosbag command-line tool to record the topic into a bag file.

   rosbag record -O my_video.bag /camera/image_raw
   

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

roslaunch svo_ros test_rig3.launch

Start RVIZ (Robot Visualizer) in a new console

rosrun rviz rviz -d <PATH TO rpg_svo>/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 my_video.bag

Now you should see the video with tracked features (green) and in RViz how the camera moves.

For more instructions visit SVO wiki. (https://github.com/uzh-rpg/rpg_svo/wiki)