Variational Bayesian Based Localization for Intelligent Vehicle Using Lidar and GPS Data Fusion: Algorithm and Experiments

• Published in: IEEE/ASME transactions on mechatronics Vol. 27; no. 6; pp. 5659 - 5667
• Main Authors: Zhu, Hao, Wang, Qiao, Li, Yongfu, Leung, Henry
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Bayesian analysis; Data integration; Global Positioning System; Global positioning systems; GPS; Intelligent vehicle (IV); Intelligent vehicles; Laser radar; Lidar; localization; Localization method; Location awareness; Loss measurement; Motion measurement; Pollution measurement; Position measurement; variational Bayesian (VB.)
• ISSN: 1083-4435; 1941-014X
• DOI: 10.1109/TMECH.2022.3187975

Accurate localization is crucial for safe operation of intelligent vehicle (IV). In practice, global positioning system (GPS) signals sometimes may be contaminated or lost, resulting in inaccurate positions of IV. In this article, the distance of IV's position between previous frame and current frame is derived from Lidar point cloud registration. A novel slide window variational Bayesian (VB) based localization method is proposed for IV with multiple dynamics by fusing GPS and Lidar. In the proposed method, the state of IV, the motion model identity of IV, the measurement loss identity, and the loss probability of measurement are jointly estimated by the VB technique. The effectiveness of the proposed localization method is validated by simulations and field experiments.