가우시안 혼합모델 기반 3차원 차량 모델을 이용한 복잡한 도시환경에서의 정확한 주차 차량 검출 방법

Abstract

Recent developments in robotics and intelligent vehicle area, bring interests of people in anautonomous driving ability and advanced driving assistance system. Especially fully automatic parkingability is one of the key issues of intelligent vehicles, and accurate parked vehicles detection is essentialfor this issue. In previous researches, many types of sensors are used for detecting vehicles, 2D LiDARis popular since it offers accurate range information without preprocessing. The L shape feature is mostpopular 2D feature for vehicle detection, however it has an ambiguity on different objects such asbuilding, bushes and this occurs misdetection problem. Therefore we propose the accurate vehicledetection method by using a 3D complete vehicle model in 3D point clouds acquired from front inclined2D LiDAR. The proposed method is decomposed into two steps: vehicle candidate extraction, vehicledetection. By combination of L shape feature and point clouds segmentation, we extract the objectswhich are highly related to vehicles and apply 3D model to detect vehicles accurately. The methodguarantees high detection performance and gives plentiful information for autonomous parking. Toevaluate the method, we use various parking situation in complex urban scene data. Experimentalresults shows the qualitative and quantitative performance efficiently.