Two-Layer-Graph Clustering for Real-Time 3D LiDAR Point Cloud Segmentation

• Published in: Applied sciences Vol. 10; no. 23; p. 8534
• Main Authors: Yang, Haozhe, Wang, Zhiling, Lin, Linglong, Liang, Huawei, Huang, Weixin, Xu, Fengyu
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.12.2020
• Subjects: Accuracy; Algorithms; Autonomous vehicles; cluster; Clustering; Design; graph structure; improved BFS; Methods; point cloud; real time; Sensors
• DOI: 10.3390/app10238534
• ISSN: 2076-3417

The perception system has become a topic of great importance for autonomous vehicles, as high accuracy and real-time performance can ensure safety in complex urban scenarios. Clustering is a fundamental step for parsing point cloud due to the extensive input data (over 100,000 points) of a wide variety of complex objects. It is still challenging to achieve high precision real-time performance with limited vehicle-mounted computing resources, which need to balance the accuracy and processing time. We propose a method based on a Two-Layer-Graph (TLG) structure, which can be applied in a real autonomous vehicle under urban scenarios. TLG can describe the point clouds hierarchically, we use a range graph to represent point clouds and a set graph for point cloud sets, which reduce both processing time and memory consumption. In the range graph, Euclidean distance and the angle of the sensor position with two adjacent vectors (calculated from continuing points to different direction) are used as the segmentation standard, which use the local concave features to distinguish different objects close to each other. In the set graph, we use the start and end position to express the whole set of continuous points concisely, and an improved Breadth-First-Search (BFS) algorithm is designed to update categories of point cloud sets between different channels. This method is evaluated on real vehicles and major datasets. The results show that TLG succeeds in providing a real-time performance (less than 20 ms per frame), and a high segmentation accuracy rate (93.64%) for traffic objects in the road of urban scenarios.