3D Reconstruction from UAV-acquired Imagery for Road Surface Distress Assessment

Road condition data are very important in transportation management system. Conventional data collection approach is time-consuming and labor intensive. This paper reports the developed techniques for 3D road surface reconstruction in support of unpaved road distress assessment using imagery acquired from a Unmanned Aviation Vehicle (UAV)-based imaging system. This system, produced in a project for rural road condition monitoring sponsored by the US Department of Transportation, consists of a low cost model helicopter equipped with a GPS/IMU, Flight Control System, a Ground Control System, and a non-metric camera. To generate road surface model, a coarse-to-fine hierarchical strategy with several image matching algorithms has been employed in this work. The road surface model is constructed by combining the matching results of feature points, normal image points and grid points. The matching strategy takes use of the short baseline of stereo imagery to avoid large image distortion while in 3D computation images with larger separation are exploited to benefit from the favorable geometry for image ray intersection. For grid point matching, a technique of cross-correlation along the vertical direction is developed. This approach simultaneously determines the correspondences cross images and the 3D position of the matched points. The coarse-to-fine strategy has been designed to initially reconstruct the surface model from matched feature points and normal image points. Afterwards, evenly distributed grid points are created, with the matched grid points improving the initial surface model in areas with poor or no texture. These dense 3D points reproduce detailed road surface which characterizes the details of the road distresses, thus facilitating road condition evaluation. Experiments have been conducted over several road segments and 3D models of different types of road distresses are reproduced to demonstrate the capability of the developed algorithms.