Normal Distribution Mixture Matching Based Model Free Object Tracking Using 2D LIDAR

Baehoon Choi1, Hyunggi Jo1, Euntai Kim1

  • 1Yonsei University

Details

11:45 - 12:00 | Tue 5 Nov | LG-R12 | TuAT12.4

Session: Human Detection and Tracking

Abstract

In this paper, a novel normal distribution mixture matching based model free object tracking algorithm using 2D LIDAR is proposed. Each target object is modeled as a normal distribution mixture that captures the distribution of the points scanned from the surface of the object. This novel representation enables normal distribution transform (NDT) to accurately estimate the motion of objects, even if the shape of the points differs depending on where it is observed. Our evaluation of the proposed algorithm shows good performance in practical applications. In addition, we provides an alternative way of segmentation and data association using occupancy grid map to avoid a problem that defines a distance metric between the mixture and the point cloud. As a result, the proposed algorithm works in real time in our experiments.