Journal of Auto-vehicle Safety Association (자동차안전학회지)

Korean Auto-vehicle Safety Association (한국자동차안전학회)
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LiDAR based Real-time Ground Segmentation Algorithm for Autonomous Driving

자율주행을 위한 라이다 기반의 실시간 그라운드 세그멘테이션 알고리즘

  • 이아영 (서울대학교 기계공학부) ;
  • 이경수 (서울대학교 기계공학부)
  • Received : 2021.05.25
  • Accepted : 2022.03.18
  • Published : 2022.06.30
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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.

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Acknowledgement

본 연구는 국토교통부 및 국토교통과학기술 진흥원의 2018년 교통물류연구사업(18TLRP-B146733-01, 자율주행기반 대중교통시스템 실증 연구)의 지원을 받아 연구되었음을 밝히며, 이에 감사드립니다.

References

  1. Banks, Victoria A., Katherine L. Plant, and Neville A. Stanton, "Driver error or designer error: Using the Perceptual Cycle Model to explore the circumstances surrounding the fatal Tesla crash on 7th May 2016", Safety science 108 (2018): 278~285. https://doi.org/10.1016/j.ssci.2017.12.023
  2. AutoNet2030, 2014, Co-operative Systems in Support of Networked Automated Driving by 2030.
  3. Wolcott, Ryan W., and Ryan M. Eustice, "Visual localization within lidar maps for automated urban driving", 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, 2014.
  4. Li, You, and Javier Ibanez-Guzman, "Lidar for autonomous driving: The principles, challenges, and trends for automotive lidar and perception systems", IEEE Signal Processing Magazine 37.4 (2020): 50~61. https://doi.org/10.1109/msp.2020.2973615
  5. Martin A. Fischler and Robert C. Bolles, 1981, Random Sample Consensus: A Paradigm for Model Fitting with Application to Image Analysis and Automated Cartography.