An Underground Parking Lot Mask for the Long-Term Visual Localization of Intelligent Vehicles

Abstract

In the underground parking lot (UPL) scenario, appearance changes in an indoor environment cause visual feature matching failures between features extracted from the image and features in the map, and further, lead to vehicle localization loss. In this article, a prior UPL mask is proposed for long-term vehicle localization. First, the UPL polygonal pattern is summarized based on structured environment characteristics. Then, feature matching is performed on the unannotated video data that reflect the degree of environmental change, and the polygonal pattern parameters are determined by a rule-based method using the statistical results of feature matching. Thus, the main distribution area of long-term static features in the mask is obtained quantitatively. Finally, the extraction of features in the mask for mapping and localization allows more static feature pairs to be matched for motion estimation. A real vehicle platform with stereo vision was set up for experimental verification. The comparison results demonstrated that the proposed method improved long-term vehicle localization stability and adaptability to overcome environmental changes.