Journal of Auto-vehicle Safety Association (자동차안전학회지)

Korean Auto-vehicle Safety Association (한국자동차안전학회)
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LiDAR Static Obstacle Map based Vehicle Dynamic State Estimation Algorithm for Urban Autonomous Driving

도심자율주행을 위한 라이다 정지 장애물 지도 기반 차량 동적 상태 추정 알고리즘

  • 김종호 (서울대학교 기계항공공학부) ;
  • 이호준 (서울대학교 기계항공공학부) ;
  • 이경수 (서울대학교 기계항공공학부)
  • Received : 2021.12.15
  • Accepted : 2021.10.04
  • Published : 2021.12.31
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Abstract

This paper presents LiDAR static obstacle map based vehicle dynamic state estimation algorithm for urban autonomous driving. In an autonomous driving, state estimation of host vehicle is important for accurate prediction of ego motion and perceived object. Therefore, in a situation in which noise exists in the control input of the vehicle, state estimation using sensor such as LiDAR and vision is required. However, it is difficult to obtain a measurement for the vehicle state because the recognition sensor of autonomous vehicle perceives including a dynamic object. The proposed algorithm consists of two parts. First, a Bayesian rule-based static obstacle map is constructed using continuous LiDAR point cloud input. Second, vehicle odometry during the time interval is calculated by matching the static obstacle map using Normal Distribution Transformation (NDT) method. And the velocity and yaw rate of vehicle are estimated based on the Extended Kalman Filter (EKF) using vehicle odometry as measurement. The proposed algorithm is implemented in the Linux Robot Operating System (ROS) environment, and is verified with data obtained from actual driving on urban roads. The test results show a more robust and accurate dynamic state estimation result when there is a bias in the chassis IMU sensor.

Keywords

Acknowledgement

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

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