R2-LIO: Real-Time and Robust LiDAR-Inertial Odometry in Dynamic Environments
Gu Changjun, Ziyi Huang, Gan Sun, Jiahua Dong, Jiaxu Leng, Xinbo Gao
AI summary
Problem
Most solid-state LiDAR-inertial odometry methods assume static environments, causing accuracy degradation and instability when dynamic objects like vehicles and pedestrians occupy a large portion of the sensor's narrow field of view.
Approach
The framework tracks voxel state changes over time to identify and remove dynamic points, then integrates a line-search mechanism into the Error State Iterated Kalman Filter to ensure stable convergence even with limited static features.
Key results
- Surpasses state-of-the-art LIO methods on YULAN and HeLiPR datasets
- Reduces localization error in highly dynamic urban scenarios
- Stabilizes Kalman filter updates under limited static feature constraints
- Enables real-time dynamic point removal for solid-state LiDAR
Why it matters
Enables reliable autonomous navigation and mapping for robots operating in complex, dynamic urban environments where traditional odometry fails.
Abstract
LiDAR-Inertial Odometry (LIO) is crucial for robot navigation and autonomous exploration. 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 removes dynamic objects to improve the localization accuracy and robustness. Specifically, we design a dynamic point removal mechanism based on voxel state changes, which removes dy- namic points and preserves 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.