Lane-Level Map-Aided SLAM Approach for Persistent Positioning in GNSS-Denied Areas

作者：Yafei Liu, Wanqi Jiang, Xiaoguo Zhang · 发表于：IEEE Transactions on Instrumentation and Measurement · 年份：2025 · DOI：10.1109/tim.2025.3545891 · 被引用次数：4 · 研究领域：Robotics and Sensor-Based Localization、Indoor and Outdoor Localization Technologies、Robotic Path Planning Algorithms

Accurate positioning is essential for the navigation of autonomous vehicles. Although the Global Navigation Satellite System (GNSS) can provide high-precision positioning capabilities for vehicles in open environments, its positioning capabilities face significant challenges in urban environments with high levels of obstruction and severe multipath effects. In GNSS-denied environments, recursive positioning systems like inertial navigation systems (INSs), LiDAR/vision simultaneous localization and mapping (SLAM), and so on, can provide positioning capabilities, but the error characteristics of their measurement sensors cause the accumulated error of the positioning system to easily diverge over time. Therefore, this article proposes a novel factor graph optimization (FGO) framework for real-time vehicle navigation. This framework integrates visual inertial and map information, which obtains the normal vehicle-to-lane distance between by lane detection in real-time, and adds the measurement constraints of high-definition (HD) vectorized road network maps to the sliding window-based FGO. This joint optimization uses a constructed lane-level map-matching algorithm to achieve the elimination of the cumulative positioning error of the vehicle in GNSS-denied environments. The navigation performance of the proposed algorithm is evaluated through experiments on public datasets and real-world scenarios, and the results show that our method can achieve accurate positioning in situation...