Automatic road structure detection and vectorization using mls point clouds

Abstract

Accurate three-dimensional road structures and models are of great significance to intelligent transportation applications, such as vehicle navigation, inventory evaluation, construction quality control, self-driving vehicles and so on. This paper proposes an efficient and robust method to automatically extract structured road curbs from mobile laser scanning (MLS) data. The proposed method mainly consists of three steps: efficient supervoxel generation, road curbs detection and driving free space estimation. First, supervoxels are generated by assigning ground points with similar geometrical characteristics into the same group. Second, supervoxels with higher local projection density and height difference are identified and clustered as initial road curbs, which are continuous vertical curb facets. The continuous facades consisting of lots of scanned points on the road shoulder can be modeled as multi-dimensional boundary models depending on the requirements of the application, such as vector lines with or without height, micro-facades, etc. Finally, driving free space is obtained due to the road limits can be defined by road boundary in most scenarios. The proposed method is tested on two complex datasets acquired by an Alpha3D mobile laser scanning system from the urban area of Shanghai, China. Experimental results show that the road boundaries and driving free space can be accurately and efficiently extracted, which also demonstrates the superiority of the proposed method.