Abstract:

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.

Key words: intelligent vehicle, vision-based positioning, second-order Hidden Markov Model, visual map