Authors: Yoshiyuki Yamamoto, Yasuhiro Shimizu, Eiji Nakamura, Masayuki Okugawa, Tomohito Asaka, and Keishi Iwashita
Mobile Mapping Systems (MMSs) have attracted considerable attention from the viewpoint of application to road management and maintenance. MMSs can help evaluate the condition of asphalt pavements. In a road space, it is essential to keep the road surface in good condition, without surface damage such as cracks and patches. Human inspection is still widely used for road management to detect such damage. However, manual surveys are time-consuming and not very effective. Therefore, the successful automation of surface damage surveys will help reduce the cost of detecting damage and provide more objective and standardized results for road management. Our previous study attempted to visualize surface normals for point clouds of asphalt pavements in both good and poor conditions. However, our visualization was unable to help us clearly discriminate between good and bad surface pavement conditions. Thus, the purpose of this study is to examine and test a method that uses MMS to evaluate road conditions effectively. We try to evaluate surface conditions through colored point clouds assigned values of 8-bit RGB color components that map the values of the surface normal and the curvature in a point cloud. An experiment shows that our method generates point clouds that successfully enable us to distinguish between good and bad road surface conditions. Our result also indicates the importance of selecting optimal values for the radius of the point cloud in calculating curvature.
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