Abstract
Mobile wheel-based LiDAR systems are capable of collecting a dense, accurate point cloud efficiently. Such systems provide accurate geospatial information of a scene, and thus show great potential to serve as an alternative infrastructure monitoring tool. In this study, two different surveys of runway 10/28 at Purdue University Airport (KLAF) were conducted. The surveys were performed in April and July of 2018 before and after a partial runway pavement resurfacing project. The study had two objectives: (1) evaluate the positional accuracy of derived features, and (2) evaluate LiDAR’s ability to detect changes in longitudinal/transversal profiles. The former used 10 static features in the point cloud; the latter used longitudinal and transversal profiles over a 1 mile-long runway section. For the 10 static features (runway lights), the root mean square errors (RMSE) of the coordinate differences are 0.13 ft, 0.08 ft, and 0.09 ft along the x, y, and z-directions, respectively. Considering the range and positional accuracy of the LiDAR and implemented GNSS/INS units, respectively, these RMSE values are acceptable. The derived numerical results from the longitudinal and cross-sectional profiles showed close alignment of pavement elevation where there was no construction activity and a 0.3 ft increase in elevation for the pavement section that was resurfaced. This paper provides a mechanism for evaluating the spatial accuracy of mobile LiDAR data and demonstrates its potential for mapping airfield infrastructure. As the technology improves, it is anticipated that cost-effective solutions yielding sub-centimeter accuracy will be available in next 18 months. Such availability, together with the established repeatability from this study, will stimulate more research and practical applications implementing mobile LiDAR systems.
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