Lidar-Monocular Visual Odometry. This library is designed to be an open platform for visual odometry algortihm development. We focus explicitely on the simple integration of the following key methodologies:

• Keyframe selection
• Landmark selection
• Prior estimation
• Depth integration from different sensors.
• Scale integration by groundplane constraint.

The core library keyframe_bundle_adjustment is a backend that should faciliate to swap these modules and easily develop those algorithms.

• It is supposed to be an add-on module to do temporal inference of the optimization graph in order to smooth the result

• In order to do that online a windowed approach is used

• Keyframes are instances in time which are used for the bundle adjustment, one keyframe may have several cameras (and therefore images) associated with it

• The selection of Keyframes tries to reduce the amount of redundant information while extending the time span covered by the optimization window to reduce drift

• Methodologies for Keyframe selection:

  • Difference in time
  • Difference in motion

• We use this library for combining Lidar with monocular vision.

• Limo2 on KITTI is LIDAR with monocular Visual Odometry, supported with groundplane constraint

• Video: https://youtu.be/wRemjJBjp64

This work was accepted on IROS 2018. See https://arxiv.org/pdf/1807.07524.pdf .

If you refer to this work please cite:

@inproceedings{graeter2018limo, title={LIMO: Lidar-Monocular Visual Odometry}, author={Graeter, Johannes and Wilczynski, Alexander and Lauer, Martin}, booktitle={2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, pages={7872--7879}, year={2018}, organization={IEEE} }

Please note that LIMO2 differs from the publication. We enhanced the speed a little and added additional groundplane reconstruction for pure monocular visual odometry and a combination of scale from LIDAR and the groundplane (best performing on KITTI).

In any case:

• ceres:
  • follow the instructions on http://ceres-solver.org/installation.html
  • you will need sudo make install to install the headers.
  • tested with libsuitesparse-dev from standard repos.
• png++:

 sudo apt-get install libpng++-dev

• install ros:
  • follow the instructions on https://wiki.ros.org/kinetic/Installation.
  • you will need to install ros-full (for pcl).
  • don't forget to source your ~/.bashrc afterwards.
• install catkin_tools:

sudo apt-get install python-catkin-tools

• install opencv_apps:

sudo apt-get install ros-kinetic-opencv-apps

• install git:

sudo apt-get install git

• initiate a catkin workspace:

  cd ${your_catkin_workspace}
  catkin init

• clone limo into src of workspace:

  mkdir ${your_catkin_workspace}/src
  cd ${your_catkin_workspace}/src
  git clone https://github.com/johannes-graeter/limo.git

• clone dependencies and build repos

  cd ${your_catkin_workspace}/src/limo
  bash install_repos.sh

• unittests:

  cd ${your_catkin_workspace}/src/limo
  catkin run_tests --profile limo_release

• get test data from https://www.mrt.kit.edu/graeterweb/04.bag

  1. this is a bag file generated from Kitti sequence 04 with added semantic labels.
  2. there is more under the same address all named ??.bag (supported: 00.bag, 01.bag, 04.bag)

• in different terminals

  1. roscore
  2. rosbag play 04.bag -r 0.1 --pause --clock

  3. source ${your_catkin_workspace}/devel_limo_release/setup.sh
     roslaunch demo_keyframe_bundle_adjustment_meta kitti_standalone.launch

  4. unpause rosbag (hit space in terminal)
  5. rviz -d ${your_catkin_workspace}/src/demo_keyframe_bundle_adjustment_meta/res/default.rviz

• watch limo trace the trajectory in rviz :)

• Before submitting an issue, please have a look at the section Known issues.

• runtime is ok for individual modules, however communication between nodes must be enhanced to ensure online usage (nodelets...).
• Make it overall faster.
• Test on our own test car.

If you just want to give it a quick peek, I prepared a ready-to-use virtualbox image (packed with Ubuntu 16.04.04, ros kinetic and all dependencies for limo).

• download it from https://www.mrt.kit.edu/graeterweb/limo_full.ova.
• Password for the vm-image is "1234".
• Find all modules in ~/workspaces/limo/ .
• Run example (~/04.bag) as described above.
• Note that the runtime in the virtual machine is slower than on a normal system.

• Unittest of LandmarkSelector.voxel fails with libpcl version 1.7.2 or smaller (just 4 landmarks are selected). Since this works with pcl 1.8.1 which is standard for ros melodic, this is ignored. This should lower the performance of the software only by a very small amount.