Abstract
3D point clustering is important for the LiDAR perception system involved applications in tracking, 3D detection, etc. With the development of high-resolution LiDAR, each LiDAR frame perceives richer detail information of the surrounding environment but highly enlarges the point data volume, which brings a challenge for clustering algorithms to precisely segment the point cloud while running with a real-time processing speed. To meet this challenge, we innovate a multi-view (bird’s eye view and front view) based clustering method, named MVC. The method contains two stages. In the first stage, we propose a density image based algorithm, PG-DBSCAN, to segment the point cloud in bird’s eye view (BEV), which derives the preliminary division with fairly low computation resources. Then in the second stage, a front view (FV) clustering process is integrated to refine the under-segmented clusters. Our method takes both the speed and precision advantages of BEV and FV clustering, and this coarse-to-fine architecture reasonably allocates the computation resources and shows a real-time outstanding clustering performance. We evaluate the MVC algorithm both on the publicly available dataset with 64-line LiDAR and our own dataset with 128-line LiDAR. Compared with other clustering methods, MVC is able to derive more accurate clustering results. Specifically, toward the 128-line LiDAR with large data volume, our method shows an outperforming running speed, which perfectly fits on the LiDAR perception tasks.
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Jie, H., Ning, Z., Zhao, Q., Liu, W., Hu, J., Gao, J. (2022). Multi-view Based Clustering of 3D LiDAR Point Clouds for Intelligent Vehicles. In: Aziz, H., Corrêa, D., French, T. (eds) AI 2022: Advances in Artificial Intelligence. AI 2022. Lecture Notes in Computer Science(), vol 13728. Springer, Cham. https://doi.org/10.1007/978-3-031-22695-3_5
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