TOWARDS AUTONOMOUS HIGH-DEFINITION MAP CONSTRUCTION

Abstract

This paper presents a GPS constrained SLAM solution, which adds reliable GPS observations as key frames to the state-of-the-art SLAM algorithm Lightweight and Ground-Optimized Lidar Odometry and Mapping (LeGO-LOAM). As the GPS has a much higher frequency than that of lidar, we first assign each lidar frame with GPS time, then every 5 seconds, a reliable GPS observation is inserted as a key frame to the pose graph, then optimizes the pose estimation and updates the old key frames. We test our method with two platforms in real driving scene, and compare its performance with LeGO-LOAM, where LeGO-LOAM cannot acquire realtime efficiency. The difference of lidar odometry of our solution with compare to RTK-GPS is less than 0.4m, with the globally referenced map can be used for relocalization.