An Autonomous Driving Vehicle-Road Collaborative Heterogeneous Fusion Localization Method Based on the Epipolar Plane Model and Graph Optimization

High-precision localization is central to the safety of high-level autonomous driving. However, existing SLAM and map-based localization methods suffer from challenges, such as cumulative errors and the need for frequent map updates. To address these issues, this article proposes a vehicle–road collaborative localization fusion approach designed to achieve high-accuracy localization without cumulative errors. The proposed method employs visual-inertial odometry (VIO) on the vehicle side, where gyroscope bias is optimized through image observations and an epipolar plane model to ensure robust pose initialization. On the roadside, 3-D object detection is utilized to provide drift-free vehicle position and orientation information. By leveraging factor graph optimization and an edge-based marginalization mechanism, this approach efficiently integrates local constraints from vehicle-side VIO data with global constraints from roadside localization data, thereby reducing computational complexity. Experimental results demonstrate that incorporating roadside infrastructure significantly reduces localization errors, effectively mitigating long-term drift. Evaluations conducted in both road and indoor environments at Jiangsu University indicate that the proposed system improves localization accuracy by 30% compared to visual-inertial navigation system (VINS)-Mono. The findings validate the superiority of vehicle–road collaborative localization in both accuracy and stability, offering a promising solution for advancing intelligent driving technologies.