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

Abstract

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 paper 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 highperformance 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. INDEX TERMS Static probability, Scan-matching, Weighted NDT, LiDAR characteristic, Graph-based SLAM I. INTRODUCTION Light detection and ranging (LiDAR), which is an essential sensor in autonomous vehicles, has the following two characteristics. First, it can provide precise distance information on vehicles, buildings, and people in the form of points. Second, a LiDAR sensor can represent the surroundings of

Kalman-Based Scene Flow Estimation for Point Cloud Densification and 3D Object Detection in Dynamic Scenes

“…Further innovations include the development of low-latency alignment methods suitable for real-world applications, which combine adaptive thresholding ICP, robustness kernels, motion compensation, and downsampling strategies [28]. In dynamic environments, enhanced NDT algorithms have been employed to estimate the static likelihood of points, thereby efficiently handling scenes with multiple moving objects [29]. Urban scene point cloud registration has also seen significant advancements with the introduction of MLP(Multi-layer Perceptron)-based models, which estimate transformations implicitly and show promising results [14].…”

Positioning of Quadruped Robot Based on Tightly Coupled LiDAR Vision Inertial Odometer

“…In the process, it does not use the feature calculation and matching of corresponding points, which reduces the time and resource costs involved. Moreover, this algorithm has little correlation with the initial value [24]. However, NDT does not work in degraded scenes and cannot remove the noise generated by dynamic obstacles in the map.…”

“…Moreover, this algorithm has little correlation with the initial value [24]. However, NDT does not work in degraded scenes and cannot remove the noise generated by dynamic obstacles in the map.…”