Abstract
This paper presents a survey of literature about road feature extraction, giving a detailed description of a Mobile Laser Scanning (MLS) system (RIEGL VMX-450) for transportation-related applications. This paper describes the development of automated algorithms for extracting road features (road surfaces, road markings, and pavement cracks) from MLS point cloud data. The proposed road surface extraction algorithm detects road curbs from a set of profiles that are sliced along vehicle trajectory data. Based on segmented road surface points, we create Geo-Referenced Feature (GRF) images and develop two algorithms, respectively, for extracting the following: 1) road markings with high retroreflectivity and 2) cracks containing low contrast with their surroundings, low signal-to-noise ratio, and poor continuity. A comprehensive comparison illustrates satisfactory performance of the proposed algorithms and concludes that MLS is a reliable and cost-effective alternative for rapid road inspection.
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