AVP-Loc: Surround View Localization and Relocalization Based on HD Vector Map for Automated Valet Parking

Localization is a crucial prerequisite for automated valet parking, in which a vehicle is required to navigate itself in a GPS-denied parking lot. Traditional visual localization methods usually build a feature map and use it for future localizations. However, the feature map is not robust to changes in illumination, appearance, and viewing perspective. To deal with this issue, we need a more stable map. In this paper, we propose to use the parking lot’s HD vector map directly for localization. The vector representation is ultimately stable but brings challenges in data association as well. To this end, we present a novel data association method to match the surround-view images with the vector map. In addition, we also propose a closed-form relocalization strategy by exploiting distinctive road mark combinations in the vector map. Experiments show that the proposed method is able to achieve centimeter-level localization accuracy in a multi-floor parking lot.