What's in My LiDAR Odometry Toolbox?
Abstract
With the democratization of 3D LiDAR sensors, precise LiDAR odometries and SLAM are in high demand. New methods regularly appear, proposing solutions ranging from small variations in classical algorithms to radically new paradigms based on deep learning. Yet it is often difficult to compare these methods, notably due to the few datasets on which the methods can be evaluated and compared. Furthermore, their weaknesses are rarely examined, often letting the user discover the hard way whether a method would be appropriate for a use case. In this paper, we review and organize the main 3D LiDAR odometries into distinct categories. We implemented several approaches (geometric based, deep learning based, and hybrid methods) to conduct an in-depth analysis of their strengths and weaknesses on multiple datasets, guiding the reader through the different LiDAR odometries available. Implementation of the methods has been made publicly available at https://github.com/Kitware/pyLiDAR-SLAM.
Cite
@article{arxiv.2103.09708,
title = {What's in My LiDAR Odometry Toolbox?},
author = {Pierre Dellenbach and Jean-Emmanuel Deschaud and Bastien Jacquet and François Goulette},
journal= {arXiv preprint arXiv:2103.09708},
year = {2021}
}
Comments
This work was realised in the context of the PhD thesis of Pierre Dellenbach, financed by Kitware (https://www.kitware.fr/equipe-vision-par-odinateur/), under the supervision of Bastien Jacquet (Kitware), Jean-Emmanuel Deschaud and Fran\c{c}ois Goulette (Mines ParisTech)