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Visual Place Recognition via Semantic and Geometric Descriptor for Automated Valet Parking

Visual place recognition refers to identifying whether the current visual observation is from a place in a pre-built map and if so, which one. Accurate place recognition improves the localization accuracy of autonomous vehicles and helps them to park at designated places. Existing methods perform unsatisfactorily in AVP tasks due to the sparse texture of the parking lot, the varying viewpoints and the moving cars. To alleviate these problem, this paper proposes a robust and discriminative descriptor by encoding semantic information and geometric arrangement. A coarse-to-fine place recognition framework is also presented. Coarse place recognition candidates are identified by semantic information. Then, spatial consistency is performed to enhance the previous coarse results. Experiments in a parking lot demonstrate that the proposed framework achieves better performance where existing state-of-the-art methods fail.