APM-SLAM: Visual Localization for Fixed Routes with Tightly Coupled a Priori Map

Abstract

Localization along fixed routes is the fundamental function of transportation applications, including patrol vehicles, shuttles, buses, and even passenger vehicles. To achieve accurate and reliable localization, we propose a tightly coupled A Priori Map Simultaneous Localization and Mapping (APM-SLAM) system. APM-SLAM provides a comprehensive and heterogeneous framework, encompassing both mapping and localization processes. The mapping stage leverages Global Navigation Satellite System (GNSS)-aided Structure from Motion (SfM) to establish reliable a priori maps with coarse-and fine-level components. The localization process integrates coarse-to-fine matching with Maximum A Posteriori (MAP) Probability estimation to refine pose accuracy. By incorporating deep learning-based features and point descriptors, our system maintains robustness even in scenarios with significant visual variation. Unlike traditional map-based approaches, APM-SLAM models the a priori map's point structures as probabilistic distributions and incorporates them into the optimization process. Extensive experiments on public datasets demonstrate the superiority of our method in both mapping precision and localization accuracy, achieving decimeter-level translation precision. Ablation studies further validate the effectiveness of each component within our system. This work contributes to establishing maps and utilizing a priori information for localization simultaneously.