This repository implements the SLAM CT-ICP (see our article), a lightweight, precise and versatile pure LiDAR odometry.

It is integrated with the python project pyLiDAR-SLAM which gives access to more datasets. pyLiDAR-SLAM requires the installation of the python binding for CT-ICP (see below).

We introduce the ROS plugin on branch dev, it is still experimental. Significant changes in branch dev were made compared to master, thus to obtain the same results as presented in our paper use please use branch master.

.\ct_icp_build.sh Release "Unix Makefiles" ON ON  # Builds the project in "Release" mode, with "Unix Makefiles" cmake generator, with python binding and with the visualization activated
source env.sh                                     # Setup the environment (.so locations) 
.\slam -c default_config.yaml                     # Launches the SLAM

.\ct_icp_build.bat                  # Builds the project
.\env.bat                           # Setup the environment (.so locations) 
.\slam.exe -c default_config.yaml   # Launches the SLAM

To modify options (for viz3d support, or python binding) for the windows script, you can directly modify the ct_icp_build.bat file.

As a debugging/visualization tool (and until we provide a ROS support rosviz) we use a home-made/experimental lightweight OpenGL-based pointcloud visualizer viz3d designed for our SLAM use case.

The steps below will install a python package named pyct_icp:

• Generate the cmake project with the following arguments (Modify ct_icp_build.sh):

  • -DWITH_PYTHON_BINDING=ON: Activate the option to build the python binding
  • -DPYTHON_EXECUTABLE=<path-to-target-python-exe>: Path to the target python executable

• Go into the build folder (e.g cd ./cmake-Release)

• Build the target pyct_icp with make pyct_icp -j6

• Install the python project pip install ./src/binding

Note: This step is required to use CT-ICP with pyLiDAR-SLAM.

The Datasets are publicly available at: https://cloud.mines-paristech.fr/index.php/s/UwgVFtiTOmrgKp5

The folder is protected by the following password : npm3d

Each dataset is a .zip archive containing the PLY scan file with the relative timestamps for each point in the frame, and if available, the ground truth poses.

To install each dataset, simply download and extract the archives on disk. The datasets are redistributions of existing and copyrighted datasets, we only offer a convenient repackaging of these datasets.

The dataset available are the following:

Under Creative Commons Attribution-NonCommercial-ShareAlike LICENCE

• KITTI (see eval_odometry.php):
  • The most popular benchmark for odometry evaluation.
  • The sensor is a Velodyne HDL-64
  • The frames are motion-compensated (no relative-timestamps) and the Continuous-Time aspect of CT-ICP will not work on this dataset.
  • Contains 21 sequences for ~40k frames (11 with ground truth)
• KITTI_raw (see eval_odometry.php): :
  • The same dataset as KITTI without the motion-compensation, thus with meaningful timestamps.
  • The raw data for sequence 03 is not available
• KITTI_360 (see KITTI-360):
  • The successor of KITTI, contains longer sequences with timestamped point clouds.
  • The sensor is also a Velodyne HDL-64

Permissive LICENSE

• NCLT: (see nclt)
  • Velodyne HDL-32 mounted on a segway
  • 27 long sequences (up to in the campus of MICHIGAN university over a long
  • Challenging motions (abrupt orientation changes)
  • NOTE: For this dataset, directly download the Velodyne links (e.g. 2012-01-08_vel.tar). Our code directly reads the velodyne_hits.bin file.
• KITTI-CARLA: (see and cite KITTI-CARLA):
  • 7 sequences of 5000 frames generated using the CARLA simulator
  • Imitates the KITTI sensor configuration (64 channel rotating LiDAR)
  • Simulated motion with very abrupt rotations
• ParisLuco (published with our work CT-ICP, cf below to cite us):
  • A single sequence taken around the Luxembourg Garden
  • HDL-32, with numerous dynamic objects

> chmod+x ./env.sh    # Set permission on unix to run env.sh
> source env.sh            # Setup environment variables 
> ./slam -h           # Display help for the executable 

USAGE:

slam  [-h] [--version] [-c <string>] [-d <string>] [-j <int>] [-o
<string>] [-p <bool>] [-r <string>]


Where:

-c <string>,  --config <string>
Path to the yaml configuration file on disk

-o <string>,  --output_dir <string>
The Output Directory

-p <bool>,  --debug <bool>
Whether to display debug information (true by default)

--,  --ignore_rest
Ignores the rest of the labeled arguments following this flag.

--version
Displays version information and exits.

-h,  --help
Displays usage information and exits.

To run the SLAM call (on Unix, adapt for windows), please follow the following steps:

1. Modify/Copy and modify one of the default config (default_config.yaml, robust_high_frequency_config.yaml or robust_driving_config.yaml) to suit your needs. Notably: change the dataset and dataset root_path dataset_options.dataset and dataset_options.root_path.

2. Launch the SLAM with command: ./slam -c <config file path, e.g. default_config.yaml> # Launches the SLAM on the default config

3. Find the trajectory (and optionally metrics if the dataset has a ground truth) in the output directory

If you use our work in your research project, please consider citing:

@misc{dellenbach2021cticp,
  title={CT-ICP: Real-time Elastic LiDAR Odometry with Loop Closure},
  author={Pierre Dellenbach and Jean-Emmanuel Deschaud and Bastien Jacquet and François Goulette},
  year={2021},
  eprint={2109.12979},
  archivePrefix={arXiv},
  primaryClass={cs.RO}
}

• Make a first version of the documentation
• Save both poses for each TrajectoryFrame
• Fix bugs / Improve code quality (doc/comments/etc...)
• Add a wiki (documentation on the code)
• Add point-to-distribution cost
• Improve the robust regime (go faster and find parameters for robust and fast driving profile)
• Increase speed
• Add Unit Tests
• Github CI
• Improve visualization / Interaction for the OpenGL Window
• Improve the python binding (reduce the overhead)
• Write ROS packaging