R2-LIO: Real-Time and Robust LiDAR-Inertial Odometry in Dynamic Environments

Abstract

LiDAR-Inertial Odometry (LIO) is crucial for robot navigation and autonomous driving. Most existing methods rely on the assumption of a static environment, indiscriminately using all LiDAR measurements for localization. However, LiDAR data acquired in urban scenes often contain dynamic objects such as vehicles and pedestrians, which can adversely affect localization accuracy—particularly when using solid-state LiDAR with a relatively narrow field of view. To address this issue, we propose a novel Real-time and Robust solid-state LiDAR-Inertial Odometry (R2-LIO) framework that remove the dynamic objects to improve the localization accuracy and robustness. Specifically, we design a dynamic point removal mechanism based on voxel state changes, which removes dynamic points and preserving most static points to effectively reduce interference from dynamic objects. In addition, we introduce a line search mechanism into the Error State Iterated Kalman Filter (ESIKF) to improve the localization accuracy. Experimental results on the challenging YULAN and HeLiPR datasets show that R2-LIO surpasses existing methods, verifying its effectiveness in improving the localization accuracy and robustness.