Visual Map Matching Method for Intelligent Vehicles Based on Second-order HMM

被引：0
|
作者：
Zhou Z. [1 ]
Hu Z. [1 ,2 ]
Wang Z. [1 ]
Xiao H. [1 ]
机构：
[1] Intelligent Transport System Center, Wuhan University of Technology, Wuhan
[2] Chongqing Research Institute of Wuhan University of Technology, Chongqing
来源：
Qiche Gongcheng/Automotive Engineering | 2022年 / 44卷 / 02期
关键词：
Intelligent vehicle; Second-order Hidden Markov Model; Vision-based positioning; Visual map;
D O I：
10.19562/j.chinasae.qcgc.2022.02.005
中图分类号：
学科分类号：
摘要：
In this paper, the visual map matching problem is transformed into the optimal visual map node matching problem based on image sequence and a method for visual map matching method based on second-order HMM (Hidden Markov Model) is proposed. In this model, state variables are defined as high precision visual map nodes, and query images are defined as observation variables. In the state transition model, the second-order model is introduced to model the uniform motion of vehicles within a short period of time. Compared with the traditional first-order HMM, the second-order HMM method is more applicable and precise. The paper proposes to use the global image features to establish the matching relationship between the query image and the map nodes, and establish the transmission probability model from the matching Hamming distance, which can effectively improve the efficiency of map matching. Finally, the optimal matching map nodes are obtained by forward algorithm. The performance of the algorithm is verified in closed industrial park, open roads, and the public KITTI datasets, respectively. The experimental results show that the proposed second-order HMM model can effectively integrate vehicle motion information and image information, improve the stability and accuracy of matching, and the performance of the proposed algorithm outperforms traditional single frame matching and sequence matching algorithms. © 2022, Society of Automotive Engineers of China. All right reserved.
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页码：190 / 198
页数：8
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