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http://dx.doi.org/10.22680/kasa2022.14.2.051

LiDAR based Real-time Ground Segmentation Algorithm for Autonomous Driving  

Lee, Ayoung (서울대학교 기계공학부)
Yi, Kyongsu (서울대학교 기계공학부)
Publication Information
Journal of Auto-vehicle Safety Association / v.14, no.2, 2022 , pp. 51-56 More about this Journal
Abstract
This paper presents an Ground Segmentation algorithm to eliminate unnecessary Lidar Point Cloud Data (PCD) in an autonomous driving system. We consider Random Sample Consensus (Ransac) Algorithm to process lidar ground data. Ransac designates inlier and outlier to erase ground point cloud and classified PCD into two parts. Test results show removal of PCD from ground area by distinguishing inlier and outlier. The paper validates ground rejection algorithm in real time calculating the number of objects recognized by ground data compared to lidar raw data and ground segmented data based on the z-axis. Ground Segmentation is simulated by Robot Operating System (ROS) and an analysis of autonomous driving data is constructed by Matlab. The proposed algorithm can enhance performance of autonomous driving as misrecognizing circumstances are reduced.
Keywords
Autonomous Driving; Lidar Processing; Point Cloud Data; Random Sample Consensus; Real-time Performance; Robot Operating System; Ground Segmentation;
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