LA-LIO: Robust Localizability-Aware LiDAR-Inertial Odometry for Challenging Scenes

Junjie Huang, Yunzhou Zhang, Qingdong Xu, Song Wu, Jun Liu, Guiyuan Wang, Wei Liu

Abstract: Modern robotic systems are increasingly deployed in complex and diverse environments, and reliable localization under challenging conditions becomes crucial for the safe and efficient operation of these systems. The odometry based on LiDAR is prone to system collapse caused by computational divergence under conditions of aggressive motion and information deficiency in spatial geometry. To enhance the robustness of systems in challenging scenes, this work proposes LA-LIO, robust localizability-aware LiDAR inertial odometry. It mainly consists of three parts. Firstly, this paper presents a LiDAR degeneration detection method that enables stable degeneration assessment. Secondly, a method for segmenting LiDAR point clouds is proposed to alleviate the issue of excessive distortion in point clouds under aggressive motion scenes. The last is an Errors State Kalman Filter (ESKF) method with adaptive weights to utilize the existing spatial information as much as possible to improve the stability of the system in degenerated scenarios. The proposed method is evaluated and compared in multiple experiments, demonstrating the performance and reliability improvements of this approach in challenging environments.