Automatic Vector-based Road Structure Mapping Using Multi-beam LiDAR

TL;DR

This paper introduces a vector-based SLAM method using multi-beam LiDAR for precise, lightweight road structure mapping suitable for autonomous driving, demonstrating high accuracy without GPS.

Contribution

The paper presents a novel vector-based SLAM approach that directly generates HD maps from LiDAR data, improving efficiency and accuracy over traditional grid or point cloud methods.

Findings

Achieved an average matching error of 0.07 in three different scenes.

Mapped an 860-meter urban road with an average accuracy of 0.466 meters.

Demonstrated the method's effectiveness without relying on high-precision GPS.

Abstract

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.