JACIII Vol.22 No.5 pp. 593-601
doi: 10.20965/jaciii.2018.p0593


Research on Moving Target Tracking Algorithm Based on Lidar and Visual Fusion

Xiaoxiao Guo*, Yuansheng Liu**,†, Qixue Zhong**, and Mengna Chai**

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

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

Corresponding author

February 16, 2018
May 7, 2018
September 20, 2018
autonomous vehicles, target tracking, multi-sensor fusion, data association

Multi-sensor fusion and target tracking are two key technologies for the environmental awareness system of autonomous vehicles. In this paper, a moving target tracking method based on the fusion of Lidar and binocular camera is proposed. Firstly, the position information obtained by the two types of sensors is fused at decision level by using adaptive weighting algorithm, and then the Joint Probability Data Association (JPDA) algorithm is correlated with the result of fusion to achieve multi-target tracking. Tested at a curve in the campus and compared with the Extended Kalman Filter (EKF) algorithm, the experimental results show that this algorithm can effectively overcome the limitation of a single sensor and track more accurately.

Cite this article as:
X. Guo, Y. Liu, Q. Zhong, and M. Chai, “Research on Moving Target Tracking Algorithm Based on Lidar and Visual Fusion,” J. Adv. Comput. Intell. Intell. Inform., Vol.22 No.5, pp. 593-601, 2018.
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