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도심자율주행을 위한 라이다 정지 장애물 지도 기반 차량 동적 상태 추정 알고리즘 : LiDAR Static Obstacle Map based Vehicle Dynamic State Estimation Algorithm for Urban Autonomous Driving
DC Field | Value | Language |
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dc.contributor.author | 김종호 | - |
dc.contributor.author | 이호준 | - |
dc.contributor.author | 이경수 | - |
dc.date.accessioned | 2023-04-19T03:59:02Z | - |
dc.date.available | 2023-04-19T03:59:02Z | - |
dc.date.created | 2022-09-14 | - |
dc.date.issued | 2021-12 | - |
dc.identifier.citation | 자동차안전학회지, Vol.13 No.4, pp.14-19 | - |
dc.identifier.issn | 2005-9396 | - |
dc.identifier.uri | https://hdl.handle.net/10371/190482 | - |
dc.description.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. | - |
dc.language | 한국어 | - |
dc.publisher | 사단법인 한국자동차안전학회 | - |
dc.title | 도심자율주행을 위한 라이다 정지 장애물 지도 기반 차량 동적 상태 추정 알고리즘 | - |
dc.title.alternative | LiDAR Static Obstacle Map based Vehicle Dynamic State Estimation Algorithm for Urban Autonomous Driving | - |
dc.type | Article | - |
dc.identifier.doi | 10.22680/kasa2021.13.4.014 | - |
dc.citation.journaltitle | 자동차안전학회지 | - |
dc.citation.endpage | 19 | - |
dc.citation.number | 4 | - |
dc.citation.startpage | 14 | - |
dc.citation.volume | 13 | - |
dc.identifier.kciid | ART002793956 | - |
dc.description.isOpenAccess | N | - |
dc.contributor.affiliatedAuthor | 이경수 | - |
dc.description.journalClass | 2 | - |
dc.subject.keywordAuthor | 도심 자율 주행 | - |
dc.subject.keywordAuthor | 차량 동적 상태 | - |
dc.subject.keywordAuthor | 라이다 포인트 클라우드 | - |
dc.subject.keywordAuthor | 정규 분포 변환 | - |
dc.subject.keywordAuthor | 점유 격자 지도 | - |
dc.subject.keywordAuthor | 정지 장애물 | - |
dc.subject.keywordAuthor | 로봇 운영 체제 | - |
dc.subject.keywordAuthor | Urban Autonomous Driving | - |
dc.subject.keywordAuthor | Vehicle Dynamic State | - |
dc.subject.keywordAuthor | LiDAR point cloud | - |
dc.subject.keywordAuthor | Normal Distribution Transformation | - |
dc.subject.keywordAuthor | Occupancy Grid Map | - |
dc.subject.keywordAuthor | Static Obstacle | - |
dc.subject.keywordAuthor | Robot Operating System | - |
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