HIGHWAY AND AIRPORT RUNWAY PAVEMENT INSPECTION USING MOBILE LiDAR

Abstract

With an ever-increasing network of thousands of miles of pavement laid out over highways, road networks, and airport runways, their continuous monitoring is a task of utmost importance to public agencies responsible for their maintenance. The existing approaches mostly rely on a manual detection of pavement distress based on acquired image or video data – an approach that is time-consuming, costly, and whose results are subjective to the designated rater. This necessitates the need for a system that is capable of a quick data acquisition along with an efficient algorithm for the detection and quantification of pavement distress based on the acquired data. This paper proposes a LiDAR-based pavement distress detection and quantification using a mobile mapping system (MMS). Starting with a comparison of a medium-grade and high-grade MMS in terms of their accuracy and captured level of detail, this paper proves the ability of the high-grade MMS to allow the detection of shallow potholes and cracks in the pavement. Next, a fully automated algorithm is proposed to detect pavement distress from 3D point cloud followed by a quantification of the severity (in terms of the depth and volume) of the detected potholes/cracks. Finally, an experimental verification conducted over a 10 mile highway segment and two airport runway strips indicates the efficient performance of the proposed data acquisition system as well as the algorithm to report the pavement distress ranging from shallow cracks over airport runways to deeper potholes along highway segments.