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http://dx.doi.org/10.22680/kasa2021.13.4.123

Box Feature Estimation from LiDAR Point Cluster using Maximum Likelihood Method  

Kim, Jongho (서울대학교 기계공학부)
Yi, Kyongsu (서울대학교 기계공학부)
Publication Information
Journal of Auto-vehicle Safety Association / v.13, no.4, 2021 , pp. 123-128 More about this Journal
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.
Keywords
Autonomous Driving; LiDAR Point Cloud; Box Feature; Maximum Likelihood Method;
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