An Effective Way of Constructing Static Map Using 3-D LiDAR for Autonomous Navigation in Outdoor Environments

The construction of an accurate environmental map is fundamental for enabling precise localization and path planning of mobile robots. However, the existence of dynamic objects will interfere with the measurement of 3-D LiDAR, potentially leading to inconsistencies between the constructed map and the actual environment, thereby impeding successful robot navigation. To address this issue, we propose a static environmental map construction method using 3-D LiDAR. First, we use range images to extract ground points and segment nonground points from the raw LiDAR data. Then, we employ dynamic object point cloud correlations across different data frames to effectively detect and filter out dynamic objects. Finally, we use the point cloud data after filtering out dynamic objects as the input to the simultaneous localization and mapping (SLAM) algorithm to build a static global point cloud map and convert the map into the final static occupancy grid map. Experimental evaluation against the KITTI public dataset shows the effectiveness of the proposed dynamic object filtering algorithm, and we implement the relevant experimental verification on a self-built mobile robot with a 16-channel LiDAR. Furthermore, the experimental results also proved the effectiveness of the proposed method in constructing a static grid map in large outdoor scenes.