DOR-LINS: Dynamic Objects Removal LiDAR-Inertial SLAM Based on Ground Pseudo Occupancy

Simultaneous localization and mapping (SLAM) is one of the fundamental capabilities for autonomous vehicles to achieve accurate localization in dynamic urban environments. However, in real-world scenarios, the presence of moving objects such as pedestrians, bicycles, or vehicles affects the localization accuracy of SLAM and leaves behind ghost trails in the created map. Hence, it is essential for SLAM to remove dynamic objects in real-time to improve its accuracy. In this article, we present dynamic objects removal LiDAR-inertial SLAM (DOR-LINS), a real-time moving objects removal framework for in light detection and ranging (LiDAR)-Inertial SLAM. Built on the foundation of lidar inertial odometry via smoothing and mapping (LIO-SAM), DOR-LINS integrates the capability of removing dynamic objects, enabling it to remove most of the dynamic objects and achieve precise odometry. Our contributions are reflected in three aspects. First, our method extracts ground points from the current scan and clusters nonground points to segment them into static and potential dynamic clusters. Then, we divide the submap and current scan into multiple bins and introduce a novel concept called ground pseudo occupancy to describe the occupancy of each bin. Second, based on the ground pseudo occupancy, dynamic clusters, and static clusters, we propose an approach named beam tracing test (BTT) and combine it with scan ratio test (SRT) to select candidate dynamic bins. Finally, we employ a dynamic point verification algorithm to filter out actual dynamic points from these candidate dynamic bins. As experimentally evaluated on the UrbanLoco dataset, our proposed method removes many dynamic points and yields promising performance against state-of-the-art methods.