레이더 에고 모션 추정 신뢰성 향상을 위한 도플러 속도 기반 동적 물체 추적 및 제거
Doppler Velocity-based Dynamic Object Tracking and Rejection for Increasing Reliability of Radar Ego-Motion Estimation
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초록
차량의 물체 인식에 사용되던 센서인 레이더 센서를 위치 추정에 사용하기 위한 연구들이진행되고 있다. 특히 레이더 센서에서 출력되는 도플러 속도를 이용하여 동적 물체와 정적 물체를 분류하고, 정적 물체만을 이용하여 에고 모션을 계산하는 방법이 연구되었다. 기존의 동적 물체 분류에서는 RANSAC을 사용한 방법이 제시되었는데, 단 한 번의 알고리즘 실패가 큰영향을 미치는 위치 추정의 특성상 더 높은 성능을 가진 분류 방법이 필요하다. 본 논문에서는동적 물체의 추적 및 필터링을 통해 기존 방법보다 분류 성능을 높이는 방법에 대해 제안한다. 추가적으로 GMPHD 필터를 사용하여 추적 성능을 최대로 향상시킨다. 제안된 방법은 기존의방법과 비교하여 분류 정확도에서 더 높은 성능을 보였으며, 특히 알고리즘의 실패를 방지할수 있다는 것을 보인다.
Abstract
Researches are underway to use a radar sensor, a sensor used for object recognition in vehicles, for position estimation. In particular, a method of classifying dynamic and static objects using the Doppler velocity, the output from the radar sensor, and calculating ego-motion using only static objects has been researched recently. Also, for the existing dynamic object classification, several methods using RANSAC or robust filtering has been proposed. Still, a classification method with higher performance is needed due to the nature of the position estimation, in which even a single failure causes large effects. Hence, in this paper, we propose a method to improve the classification performance compared to existing methods through tracking and filtering of dynamic objects. Additionally, the method used a GMPHD filter to maximize tracking performance. In effect, the method showed higher performance in terms of classification accuracy compared to existing methods, and especially shows that the failure of the RANSAC could be prevented.
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