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JACIII Vol.22 No.5 pp. 602-610
doi: 10.20965/jaciii.2018.p0602
(2018)

Paper:

Dynamic Obstacle Detection and Tracking Based on 3D Lidar

Qixue Zhong*, Yuansheng Liu*, Xiaoxiao Guo**, and Lijun Ren**

*Beijing Engineering Research Center of Smart Mechanical Innovation Design Service, Beijing Union University
No.97 Beisihuan East Road, Chao Yang District, Bejing 100101, China

**Beijing Key Laboratory of Information Service Engineering, Beijing Union University
No.97 Beisihuan East Road, Chao Yang District, Bejing 100101, China

Received:
February 21, 2018
Accepted:
May 15, 2018
Published:
September 20, 2018
Keywords:
autonomous vehicles, clustering, MHT, spatio-temporal characteristics, nearest neighbor
Abstract

Detection and tracking of dynamic obstacle is one of the research hotspot in autonomous vehicles. In this paper, a dynamic obstacle detection and tracking method based on 3D lidar is proposed. The nearest neighborhood method is used to cluster the data obtained by the laser lidar. The characteristic parameters of the clustering obstacles are analyzed. Multiple hypothesis tracking model (MHT) algorithm and the nearest neighbor association algorithm are used for data association of two consecutive frames of obstacle information. The dynamic and static state of obstacles are analyzed through the temporal and spatial correlation of the obstacle. Finally, we use linear Kalman filter to predict the movement state of the obstacle. The experimental results on a low-speed driverless vehicle “small whirlwind” which is an autonomous sightseeing vehicle show that the method can accurately detect the dynamic obstacles in unknown environment with effectiveness and real-time performance.

Cite this article as:
Q. Zhong, Y. Liu, X. Guo, and L. Ren, “Dynamic Obstacle Detection and Tracking Based on 3D Lidar,” J. Adv. Comput. Intell. Intell. Inform., Vol.22, No.5, pp. 602-610, 2018.
Data files:
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Last updated on Oct. 19, 2018