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Lidar Based Object Recognition and Classification

자율주행을 위한 라이다 기반 객체 인식 및 분류

  • 변예림 (한국교통대학교 전자공학과) ;
  • 박만복 (한국교통대학교 전자공학과)
  • Received : 2020.10.05
  • Accepted : 2020.12.04
  • Published : 2020.12.31

Abstract

Recently, self-driving research has been actively studied in various institutions. Accurate recognition is important because information about surrounding objects is needed for safe autonomous driving. This study mainly deals with the signal processing of LiDAR among sensors for object recognition. LiDAR is a sensor that is widely used for high recognition accuracy. First, we clustered and tracked objects by predicting relative position and speed of objects. The characteristic points of all objects were extracted using point cloud data of each objects through proposed algorithm. The Classification between vehicle and pedestrians is estimated using number of characteristic points and distances among characteristic points. The algorithm for classifying cars and pedestrians was implemented and verified using test vehicle equipped with LiDAR sensors. The accuracy of proposed object classification algorithm was about 97%. The classification accuracy was improved by about 13.5% compared with deep learning based algorithm.

Keywords

Acknowledgement

본 연구는 국토교통부 도심도로 자율협력주행 안전·인프라 연구 사업의 연구비지원(과제번호: 20PQOW-B152618-02)에 의해 수행되었습니다. 또한 이 논문은 2020년도 정부(경찰청)의 재원으로 도로교통공단의 지원을 받아 수행된 연구임(No.POLICE-L-00001-02-101, 자율주행차의 도로주행을 위한 운행체계 및 교통인프라 연구개발) 또한 이 연구는 2019년도 산업통상자원부 및 산업기술평가관리원(KEIT) 연구비 지원에 의한 연구임(과제번호: K_G012000307003, 과제명: NCAP 대응을 위한 후방 자동제동시스템Rear Automatic Braking System 개발)

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