Journal of Auto-vehicle Safety Association (자동차안전학회지)

Korean Auto-vehicle Safety Association (한국자동차안전학회)
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Box Feature Estimation from LiDAR Point Cluster using Maximum Likelihood Method

최대우도법을 이용한 라이다 포인트군집의 박스특징 추정

  • 김종호 (서울대학교 기계공학부) ;
  • 이경수 (서울대학교 기계공학부)
  • Received : 2021.05.12
  • Accepted : 2021.10.04
  • Published : 2021.12.31
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Abstract

This paper present box feature estimation from LiDAR point cluster using maximum likelihood Method. Previous LiDAR tracking method for autonomous driving shows high accuracy about velocity and heading of point cluster. However, Assuming the average position of a point cluster as the vehicle position has a lower accuracy than ground truth. Therefore, the box feature estimation algorithm to improve position accuracy of autonomous driving perception consists of two procedures. Firstly, proposed algorithm calculates vehicle candidate position based on relative position of point cluster. Secondly, to reflect the features of the point cluster in estimation, the likelihood of the particle scattered around the candidate position is used. The proposed estimation method has been implemented in robot operating system (ROS) environment, and investigated via simulation and actual vehicle test. The test result show that proposed cluster position estimation enhances perception and path planning performance in autonomous driving.

Keywords

Acknowledgement

본 논문은 산업통상자원부 산업기술혁신사업(10079730, 자동차전용도로/도심로 자율주행 시스템 개발 및 성능평가)의 지원을 받아 수행하였습니다.

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