최대우도법을 이용한 라이다 포인트군집의 박스특징 추정 = Box Feature Estimation from LiDAR Point Cluster using Maximum Likelihood Method

다국어 초록 (Multilingual Abstract)

This paper present box feature estimation from LiDAR point cluster using maximum likelihood Method. Previous LiDAR tracking method for autonomous driving shows high accuracy about velocity and heading of point cluster. However, Assuming the average position of a point cluster as the vehicle position has a lower accuracy than ground truth. Therefore, the box feature estimation algorithm to improve position accuracy of autonomous driving perception consists of two procedures. Firstly, proposed algorithm calculates vehicle candidate position based on relative position of point cluster. Secondly, to reflect the features of the point cluster in estimation, the likelihood of the particle scattered around the candidate position is used. The proposed estimation method has been implemented in robot operating system (ROS) environment, and investigated via simulation and actual vehicle test. The test result show that proposed cluster position estimation enhances perception and path planning performance in autonomous driving.

참고문헌 (Reference)

1 이호준, "라이다를 이용한 이동 차량 검출을 위한 바운딩 박스와 가상 광선에 의한 주변 차량 형상 추출" 2018

2 Quigley, Morgan, "ROS: an open- source Robot Operating System" 3 (3): 2009

3 H. Lee, "Moving Object Detection and Tracking Based on Interaction of Static Obstacle Map and Geometric Model-Free Approach for Urban Autonomous Driving" 2020

4 Roy L. Streit, "Maximum likelihood method for probabilistic multihypothesis tracking" 2235 : 1994

5 Neubeck, Alexander, "Efficient non-maximum suppression" IEEE 3 : 2006

6 Najibi, M, "Dops: Learning to detect 3d objects and predict An LSTM Approach to Temporal 3D Object Detection in Point Clouds" 2020