Onion-LO++: An Adaptive and Degradation Resistant Continuous-Time LiDAR Odometry

Abstract

In an era dominated by multi-sensor fusion, this paper explores the operational limits of LiDAR-only odometry. We introduce Onion-LO++, which is designed to overcome two practical limitations of Onion-LO: poor performance in geometrically degenerate environments and instability under high-motion conditions. In order to mitigate point cloud degradation, we propose a coarse-to-fine point cloud segmentation approach that extracts intensity and weak corner features from planar regions, while dynamically adjusting the downsampling rate based on the proportion of planar points to maximize geometric constraints. To handle high-motion scenarios, we integrate a continuous-time trajectory model into the backend optimization and introduce an adaptive onion factor that adjusts optimization parameters in real time. Extensive experiments on five challenging public datasets demonstrate that Onion-LO++ outperforms state-of-the-art methods and operates reliably across narrow spaces, degenerate scenes, high-speed motion, and high-altitude aerial mapping. We open-source the code on GitHub.1