GR-LIO: A Local Ground-Aware LiDAR-Inertial Odometry System Using Body-to-Ground Height

2026-10-04

AI Summary

This study addresses the limitation of existing LiDAR-inertial odometry (LIO) systems that underutilize local ground geometric constraints, thereby restricting localization accuracy and computational efficiency. To this end, we propose a filter-based, local-ground-aware LIO framework. Specifically, the method parameterizes the local ground plane and incorporates point-to-plane constraints alongside planar motion updates to optimize state estimation. Furthermore, it explicitly integrates an airframe-to-ground height model to enable efficient ground segmentation and suppress vertical drift, accompanied by an online calibration strategy for this height parameter. Extensive experiments conducted on multiple public benchmarks and real-world datasets demonstrate that the proposed approach significantly outperforms representative state-of-the-art LIO methods in both localization accuracy and computational efficiency.

Abstract

LiDAR-inertial odometry (LIO) is widely used for state estimation in ground-based autonomous mobile robots. However, the geometric constraints provided by the local ground surface remain largely underexploited in existing LIO systems. This paper proposes a filter-based local ground-aware LIO framework that explicitly incorporates local ground plane geometry into the state estimation process to improve both localization accuracy and computational efficiency. Specifically, a local ground plane is parameterized by the robot orientation and the body-to-ground (B-G) height and continuously propagated within the state estimation process. Based on the proposed B-G geometry model, a propagated local ground plane enables efficient and reliable ground segmentation. The segmented ground points are then incorporated into the filter update through point-to-plane geometric constraints, improving both state estimation accuracy and efficiency. Furthermore, a planar motion update is introduced to exploit the propagated local ground plane as an additional geometric constraint, effectively suppressing vertical drift and improving estimation robustness. To address the initially unknown B-G height, an efficient initialization strategy is developed, followed by an online calibration procedure for continuous refinement. The proposed system is evaluated on several public benchmark datasets and self-collected real-world datasets covering diverse operating scenarios. Experimental results demonstrate that the proposed method consistently outperforms representative LIO methods in terms of both localization accuracy and computational efficiency.

Problem

• LiDAR-inertial odometry
• ground geometry constraint
• state estimation
• vertical drift
• localization accuracy

Innovation

• LiDAR-Inertial Odometry
• Body-to-Ground Height
• Local Ground Plane
• Ground Segmentation
• Online Calibration