What's in My LiDAR Odometry Toolbox?
This work addresses the difficulty in comparing LiDAR odometry methods for researchers and practitioners, providing a practical toolbox and analysis, though it is incremental as it organizes and evaluates existing methods rather than proposing new ones.
The paper reviews and categorizes 3D LiDAR odometry methods, implementing geometric, deep learning, and hybrid approaches to analyze their strengths and weaknesses on multiple datasets, with implementations made publicly available.
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.