Map-based localization for intelligent vehicles based on fusion of multiple visual features in underground parking

Abstract

The ‘last mile’ problem in intelligent car localization within underground parking lots presents challenges such as localization blind spots and feature extraction difficulties. To tackle these issues, this paper proposes a mapping and multiscale matching localization method based on the fusion of multiple feature sources using a ‘binocular mapping, monocular localization’ approach. By combining traditional features with deep learning features, the proposed method enhances feature matching accuracy and addresses feature extraction challenges. A sparse three-dimensional reconstruction method based on local feature-optical flow is also proposed to mitigate localization blind spots. During the mapping process, the underground parking lot scene is segmented into several nodes to store map information, each consisting of a feature layer, scene structure layer, and trajectory layer. In the multiscale matching localization process, ORB, LoFTR, and BoW multisource features are fused to achieve feature matching, determine the nearest node, and estimate vehicle pose using the PnP model through optical flow. Finally, a localization optimization algorithm outputs the localization result. Experiments were conducted across multiple underground parking lots encompassing a total area of over 100,000 square meters. The results demonstrated that the average error of map construction using the proposed algorithm is approximately 15 mm, and the average error rate of localization is 4.45%.