What's in My LiDAR Odometry Toolbox?

TL;DR

This paper reviews and categorizes 3D LiDAR odometry methods, implementing various approaches to analyze their strengths and weaknesses across multiple datasets, aiding users in selecting appropriate solutions.

Contribution

It provides a comprehensive organization of LiDAR odometry methods and offers an in-depth comparative analysis with publicly available implementations.

Findings

Deep learning methods show promising accuracy improvements.

Hybrid approaches balance robustness and computational efficiency.

Geometric methods excel in real-time applications.

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.