3-D Road Boundary Extraction From Mobile Laser Scanning Data via Supervoxels and Graph Cuts

Abstract

Effective extraction of road boundaries plays a significant role in intelligent transportation applications, including autonomous driving, vehicle navigation, and mapping. This paper presents a new method to automatically extract 3-D road boundaries from mobile laser scanning (MLS) data. The proposed method includes two main stages: supervoxel generation and 3-D road boundary extraction. Supervoxels are generated by selecting smooth points as seeds and assigning points into facets centered on these seeds using several attributes (e.g., geometric, intensity, and spatial distance). 3-D road boundaries are then extracted using the α-shape algorithm and the graph cuts-based energy minimization algorithm. The proposed method was tested on two data sets acquired by a RIEGL VMX-450 MLS system. Experimental results show that road boundaries can be robustly extracted with an average completeness over 95%, an average correctness over 98%, and an average quality over 94% on two data sets. The effectiveness and superiority of the proposed method over the state-of-the-art methods is demonstrated.