Visual Localization for Autonomous Driving using Pre-built Point Cloud Maps

This paper presents a vision-based metric localization method using pre-built point cloud maps. Matching the 3D structures reconstructed by visual SLAM to the point cloud map resolves the accumulative errors and scale ambiguity. In addition to the accuracy improvement, the proposed method achieves localization within given maps while ordinary visual SLAM constructs an on-line map and can only localize within this. Localization within a given map is crucial for autonomous driving, where various map types are employed. Point cloud maps are robust to appearance changes caused by illumination and seasonal changes. Once LiDAR sensors have built the point cloud maps, this paper demonstrates that localization is possible using solely low-cost and lightweight cameras. We verified the accuracy of the proposed method using real-world datasets. The results show that the accumulated error is suppressed even on extended vehicle trajectories. Also, we conducted experiments with various camera configurations and confirmed that the point cloud map improved the localization results for all configurations,