LiDAR based Real-time Ground Segmentation Algorithm for Autonomous Driving

Ayoung Lee; Kyongsu Yi

doi:10.22680/kasa2022.14.2.051

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2022 Vol.14, Issue 2 Preview Page Next Page

LiDAR based Real-time Ground Segmentation Algorithm for Autonomous Driving 자율주행을 위한 라이다 기반의 실시간 그라운드 세그멘테이션 알고리즘

30 June 2022. pp. 51-56

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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.

Keywords

Autonomous Driving

Lidar Processing

Point Cloud Data

Random Sample Consensus

Real-time Performance

Robot Operating System

Ground Segmentation

키워드

자율주행

라이다 처리

점군집 데이터

랜덤 표본 합의

실시간 처리

로봇 운영 체제

땅 분류

References

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AutoNet2030, 2014, Co-operative Systems in Support of Networked Automated Driving by 2030.

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. 10.1109/IROS.2014.6942558

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. 10.1109/MSP.2020.2973615

Martin A. Fischler and Robert C. Bolles, 1981, Random Sample Consensus: A Paradigm for Model Fitting with Application to Image Analysis and Automated Cartography. 10.1145/358669.358692

Information

Publisher :Korean Auto-vehicle Safety Association
Publisher(Ko) :한국자동차모빌리티안전학회
Journal Title :Journal of Auto-vehicle Safety Association
Journal Title(Ko) :자동차안전학회지
Volume : 14
No :2
Pages :51-56
Received Date : 2021-05-25
Revised Date : 2021-11-10
Accepted Date : 2022-03-18
DOI :https://doi.org/10.22680/kasa2022.14.2.051

Journal of Auto-vehicle Safety Association ISSN:2005-9396(Print) 자동차안전학회지

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