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README.md

Limo

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

  • Now we switched from kinetic to melodic

Details

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). For information on Limo2, please see my dissertation https://books.google.de/books?hl=en&lr=&id=cZW8DwAAQBAJ&oi .

Installation

Docker

To facilitate the development I created a standalone dockerfile.

# This is where you put the rosbags this will be available at /limo_data in the container
mkdir $HOME/limo_data
cd limo/docker
docker-compose build limo
  • You can run the docker and go to the entrypoint with
docker-compose run limo bash

Go to step Run in this tutorial and use tmux for terminals.

  • You can invoke a jupyter notebook with a python interface for limo with
docker-compose up limo

and open the suggested link from the run output in a browser.

Semantic segmentation

The monocular variant expects semantic segmentation of the images. You can produce this for example with my fork from NVIDIA's semantic segmentation:

  1. Clone my fork
git clone https://github.com/johannes-graeter/semantic-segmentation
  1. Download best_kitti.pth as described in the README.md from NVIDIA and put it in the semantic-segmentation folder

  2. I installed via their docker, for which you must be logged in on (and register if necessary) https://ngc.nvidia.com/

  3. Build the container with

docker-compose build semantic-segmentation
  1. Run the segmentation with
docker-copmose run semantic-segmentation

Note that without a GPU this will take some time. With the Nvidia Quadro P2000 on my laptop i took around 6 seconds per image.

Requirements

In any case:

 sudo apt-get install libpng++-dev
  • install ros:
  • install catkin_tools:
sudo apt-get install python-catkin-tools
  • install opencv_apps:
sudo apt-get install ros-melodic-opencv-apps
  • install git:
sudo apt-get install git

Build

  • 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

Run

  • get test data Sequence 04 or Sequence 01. This is a bag file generated from Kitti sequence 04 with added semantic labels.

  • in different terminals (for example with tmux)

    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.

Known issues

  • 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.