자율주행 인지를 위한 마코브 모델 기반의 정지 장애물 추정 연구

Abstract

This paper presents a new method for construction of a static obstacle map. A static obstacle is important since it is utilized to path planning and decision. Several established approaches generate static obstacle map by grid method and counting algorithm. However, these approaches are occasionally ineffective since the density of LiDAR layer is low. Our approach solved this problem by applying probability theory. First, we converted all LiDAR point to Gaussian distribution to considers an uncertainty of LiDAR point. This Gaussian distribution represents likelihood of obstacle. Second, we modeled dynamic transition of a static obstacle map by adopting the Hidden Markov Model. Due to the dynamic characteristics of the vehicle in relation to the conditions of the next stage only, a more accurate map of the obstacles can be obtained using the Hidden Markov Model. Experimental data obtained from test driving demonstrates that our approach is suitable for mapping static obstacles. In addition, this result shows that our algorithm has an advantage in estimating not only static obstacles but also dynamic characteristics of moving target such as driving vehicles.