Detecting trails in LiDAR point cloud data

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 different results in the final 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 final 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.