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14 May 2012 Detecting trails in lidar point cloud data
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Abstract
The goal of this work is to determine methods for detecting trails using statistics of LiDAR point cloud data, while avoiding reliance on a Digital Elevation Model (DEM). Creation of a DEM is a subjective process that requires assumptions be made about the density of the data points, the curvature of the ground, and other factors which can lead to very dierent results in the nal DEM product, with no single correct" result. Exploitation of point cloud data also lends itself well to automation. A LiDAR point cloud based trail detection scheme has been designed in which statistical measures of local neighborhoods of LiDAR points are calculated, image processing techniques employed to mask non-trail areas, and a constrained region growing scheme used to determine a nal trails map. Results of the LiDAR point cloud based trail detection scheme are presented and compared to a DEM-based trail detection scheme. Large trails are detected fairly reliably with some missing gaps, while smaller trails are detected less reliably. Overall results of the LiDAR point cloud based methods are comparable to the DEM-based results, with fewer false alarms.
© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Angela M. Kim and Richard C. Olsen "Detecting trails in lidar point cloud data", Proc. SPIE 8379, Laser Radar Technology and Applications XVII, 837906 (14 May 2012); https://doi.org/10.1117/12.918631
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