Abstract
LiDAR has become an indispensable sensor for autonomous vehicles due to its unique properties. The clustering of non-ground point clouds, as an essential step of the perceptual processing pipeline, is crucial to the safe driving of autonomous vehicles. However, some existing methods are prone to over-clustering, insufficient clustering, or poor real-time performance. Therefore, we propose a coarse-to-fine clustering strategy to balance clustering accuracy and speed. We first propose a clustering method based on angle and distance judgment to rough process the point cloud and then use the clustering method based on breakpoint detection to refine the point cloud. We evaluate the proposed method on two public data sets to prove the effectiveness of the proposed method. Through quantitative comparison, we find that the average processing time of each frame in the two data sets is about 14 ms, which can meet the real-time requirements of automatic driving.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
