Visual Map-Based Localization for Intelligent Vehicles Using Around View Monitoring in Underground Parking Lots

Abstract

Accurate and robust self-localization is a crucial task for intelligent vehicles. Because of limited access to GPS signals, localization in underground parking lots remains a problem. In this paper, fusion localization for intelligent vehicles using the widely available around view monitoring (AVM) is conducted by Kalman filter based on second-order Markov motion model (KF-MM2). The proposed method consists of two steps, one for visual map construction from AVM images and the other for map-based multi-scale localization. The proposed visual map consists of a series of nodes. Each node encodes both holistic and local visual features computed from AVM images, three-dimensional structure, and vehicle pose. In the localization step, the process of image-level localization is modeled as a Hidden Markov Model (HMM), in which the map nodes are hidden states. The result of image-level localization is calculated using forward algorithm by the given AVM image sequence. Then the metric localization is computed from local features matching. Finally, the metric localization is fused with the prediction by KF-MM2. The proposed method has been verified in two typical underground parking lots. Experimental results demonstrate that the proposed method can achieve an average error of 0.39 m in underground parking lots.