Multilayer map construction and vision-only multi-scale localization for intelligent vehicles in underground parking

Abstract

Abstract High-accuracy localization is a major problem for intelligent vehicles. With the continuation of traffic scenarios, underground parking lots have become an essential link to intelligent vehicle localization. Current localization methods suitable for ordinary traffic scenes, such as the global localization system and Beidou, cannot obtain stable localization results because the satellite signal is blocked. Thus, localization is a complex problem to solve in this scenario to achieve highly accurate localization. This study proposes a coarse-to-fine multi-scale localization method that can achieve highly precise localization with only one monocular camera based on the constructed multilayer map. First, the visual features of the images to be located are extracted, and the proposed features are counted and matched with the map using the bag of word model to obtain the nearest node for completing node-level coarse localization. Second, the perspective N-point problem model is formed using visual features and structural information in the nearest node. Finally, the positions and attitudes of the vehicles relative to the map node are calculated to complete degree-level fine localization. The error rate of the localization algorithm is verified to be below 7.3%, which is superior to that of other methods. This study provides a new solution for the highly accurate localization of underground parking and other indoor scenes.