Abstract: In view of the lack of GPS signal in the underground parking lots, the second-order Markov model and particle filter (MM-PF) method for intelligent vehicle localization in underground parking lots is proposed based on the construction of a visual feature map from around view. In the method, the nodes of the visual feature map are defined as particles, while the query images are defined as observation data. In the process of state transition, the second-order Markov model is introduced to model the motion of the vehicles in a short time. In addition, holistic features are employed to establish the matching relationship between the query image and each particle (visual feature map node) by assigning particle weights based on Hamming distance. In the experiments, two typical underground parking lots are selected to verify the method. In both scenarios, the mean error of localization is less than 0.38 m, the mean square error is less than 0.29 m. The probability of positioning error below 1 m is not less than 95.4%. Experimental results demonstrate that the proposed method can integrate both the motion and visual features to enhance localization performance. Experimental results also show that the proposed method outperforms state-of-the-art ones in terms of localization accuracy and robustness.

Key words: intelligent vehicle, vision localization, second-order Markov model, particle filter algorithm