Onion-LO: Why Does LiDAR Odometry Fail across Different LiDAR Types and Scenarios?

Abstract

LiDAR odometry is a fundamental technology for autonomous navigation. However, existing LiDAR-based odometry methods typically demand extensive manual parameter tuning and remain prone to instability when deployed across varying LiDAR types and environments. This letter focuses on the essence of point clouds and introduces a fast, highly adaptable, and robust LiDAR odometry framework named Onion-LO. Onion-LO demonstrates strong compatibility with various LiDAR types and reliable operation across diverse scenarios. This is facilitated by an onion-like point cloud processing structure termed Onion Ball. The Onion Ball supports multi-threaded implementation, efficiently executing point cloud distribution analysis, segmentation, and downsampling. In addition, we design an adaptive optimization strategy for local map management and iterative optimization, which effectively enhances the system's robustness and accuracy. Extensive experiments on five datasets demonstrate that Onion-LO outperforms existing state-of-the-art methods regarding localization accuracy and robustness. Additional evaluations across 11 LiDAR sensors and 8 diverse scenarios further confirm its strong generalization capability. Our method is designed for practical deployment and supports real-time operation on onboard processors. We open-source the code on https://anonymous.4open.science/r/Onion-LO.