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Quantitative Comparison of LiDAR Point Cloud Segmentation for Autonomous Vehicles
B. Anand, V. Barsaiyan, M. Senapati,
Published in Institute of Electrical and Electronics Engineers Inc.
2021
Volume: 2021-September
   
Abstract
The Light detection and ranging (LiDAR) sensor is used for perceiving the environment of an autonomous vehicle. LiDAR data or point cloud is processed to get the obstacles and their speed around an autonomous vehicle. Based on the information retrieved from LiDAR data and the data from other sensors, real-time decisions are taken for the proper navigation. Hence, the time taken in LiDAR data processing should be minimized. One of the important steps for LiDAR data processing is the segmentation of the obstacles. In this paper, we present a quantitative comparison between two different approaches for point cloud segmentation, Euclidean distance-based Cluster Extraction and Cylindrical range image-based method. Based on the simulation performed on ROS (Robot Operating System) platform, we found that the second method is much faster as compared to the first method. In addition to that, the second method can perform proper segmentation at a larger distance from the sensor. © 2021 IEEE.
About the journal
JournalData powered by TypesetIEEE Vehicular Technology Conference
PublisherData powered by TypesetInstitute of Electrical and Electronics Engineers Inc.
ISSN15502252