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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://gitlab.kitware.com/keu-computervision/pylidar-slam.
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Preprints, Working Papers, ...
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https://hal-mines-paristech.archives-ouvertes.fr/hal-03203388
Contributor : François Goulette <>
Submitted on : Tuesday, April 20, 2021 - 5:59:54 PM
Last modification on : Thursday, April 22, 2021 - 3:11:58 AM

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  • HAL Id : hal-03203388, version 1
  • ARXIV : 2103.09708

Citation

Pierre Dellenbach, Jean-Emmanuel Deschaud, Bastien Jacquet, Francois Goulette. What s in My LiDAR Odometry Toolbox?. 2021. ⟨hal-03203388⟩

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