STATIC-LIO: A sliding window and terrain-assisted dynamic points removal LiDAR Inertial Odometry

Abstract

With the development of diverse Light Detection and Ranging (LiDAR) sensors, LiDAR-based localization and mapping has become an essential issue in the fields of robotics and autonomous driving. However, the moving object in dynamic environments often introduces errors in LiDAR localization and leaves undesirable traces in the point cloud map. In this work, we propose a novel LiDAR inertial odometry (LIO) framework named STATIC-LIO, Sliding window and Terrain AssisTed dynamIC points removal LiDAR Inertial Odometry, which fuses the geometric, terrestrial, and motion information to enhance the localization and mapping performance. The terrestrial information is extracted through a fast progressive ground segmentation module designed to be compatible with various LiDARs. With the assistance of the terrestrial information, an online dynamic point voting mechanism is proposed to determine the motion information and remove the dynamic points in a point-wise manner. The ground segmentation and dynamic points removal modules are coupled within the sliding window-based STATIC-LIO framework to estimate odometry by leveraging geometric correspondences from ground and static points. We extensively evaluate the proposed framework on both public and real-world datasets encompassing a variety of LiDAR types. The experimental results demonstrate the effectiveness of STATIC-LIO across various datasets and applications, showcasing its superior accuracy by reducing localization errors by up to 92.4% compared to the state-of-the-art LIO framework.