Automatic Vector-based Road Structure Mapping Using Multi-beam LiDAR
This addresses the need for precise and lightweight HD maps in autonomous driving, though it appears incremental as it builds on existing SLAM techniques with a focus on vector representation.
The paper tackles the problem of creating vector-based road structure maps for autonomous driving by proposing a SLAM method using multi-beam LiDAR, achieving an average absolute matching error of 0.07 in tests and an average global accuracy of 0.466 meters over 860 meters without high-precision GPS.
In this paper, we studied a SLAM method for vector-based road structure mapping using multi-beam LiDAR. We propose to use the polyline as the primary mapping element instead of grid cell or point cloud, because the vector-based representation is precise and lightweight, and it can directly generate vector-based High-Definition (HD) driving map as demanded by autonomous driving systems. We explored: 1) the extraction and vectorization of road structures based on local probabilistic fusion. 2) the efficient vector-based matching between frames of road structures. 3) the loop closure and optimization based on the pose-graph. In this study, we took a specific road structure, the road boundary, as an example. We applied the proposed matching method in three different scenes and achieved the average absolute matching error of 0.07. We further applied the mapping system to the urban road with the length of 860 meters and achieved an average global accuracy of 0.466 m without the help of high precision GPS.