What's in My LiDAR Odometry Toolbox?

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 me...

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Bibliographic Details
Published in2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) pp. 4429 - 4436
Main Authors Dellenbach, Pierre, Deschaud, Jean-Emmanuel, Jacquet, Bastien, Goulette, Francois
Format Conference Proceeding
LanguageEnglish
Published IEEE 27.09.2021
Subjects
Deep learning
Distance measurement
Laser radar
Pipelines
Sensors
Simultaneous localization and mapping
Three-dimensional displays
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Summary: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.
ISSN:2153-0866
DOI:10.1109/IROS51168.2021.9636348