Robust 3-Dimension Point Cloud Mapping in Dynamic Environment Using Point-Wise Static Probability-Based NDT Scan-Matching

Graph-based simultaneous localization and mapping (SLAM) is one of the methods to generate point cloud maps which are used for various applications in autonomous vehicles. Graph-based SLAM represents the pose of the vehicle as a node and the odometry between two different nodes as an edge. Among the edge generating methods, scan matching, light detection and ranging (LiDAR) based method, can provide an accurate pose between two nodes based on the high distance accuracy of the LiDAR. However, the point cloud in real driving situations contains numerous moving objects, which degrade the scan-matching performance. Therefore, this article defines the static probability which means the likelihood that an acquired point is from a static object, and proposes the weighted normal distribution transformation (NDT), which is achieved by modifying NDT. Weighted NDT is a scan-matching algorithm which can reflect the static probability of each point as a weight. The odometry from the weighted NDT is utilized for graph construction to generate a robust point cloud map even in a dynamic environment. Finally, the proposed algorithm was compared with the existing object removal algorithms in two areas: dynamic object classification and scan-matching performance. Based on the scan-matching results, the accuracy of the point cloud map generated by the proposed algorithm was evaluated with a reference map using high-performance global navigation satellite system (GNSS). It was confirmed that the proposed algorithm has higher classification accuracy and lower scan-matching error compared with other dynamic object removal methods. The proposed algorithm was able to generate a point cloud map, despite the presence of many dynamic objects, that was similar to a map generated in the absence of dynamic objects in the same environment.