Techniques for Inferring Terrain Parameters Related to Ground Vehicle Mobility Using UAV born IFSAR and LIDAR Data

Predicting ground vehicle performance requires in-depth knowledge, captured as numeric parameters, of the terrain on which the vehicles will be operating. For off-road performance, predictions are based on rough terrain ride comfort, which is described using a parameter entitled root-mean-square (RMS) surface roughness. Likewise, on-road vehicle performance depends heavily on the slopes of the individual road segments. Traditional methods of computing RMS and road slope values call for high-resolution (inch-scale) surface elevation data. At this scale, surface elevation data is both difficult and time consuming to collect. Nevertheless, a current need exists to attribute large geographic areas with RMS and road slope values in order to better support vehicle mobility predictions, and high-resolution surface data is neither available nor collectible for many of these regions. On the other hand, meter scale data can be quickly and easily collected for these areas using unmanned aerial vehicle (UAV) based IFSAR and LIDAR sensors. A statistical technique for inferring RMS values for large areas using a combination of fractal dimension and spectral analysis of five-meter elevation data is presented. Validation of the RMS prediction technique was based on 43 vehicle ride courses with 30-centimeter surface elevation data. Also presented is a model for classifying road slopes for long road sections using five-meter elevation data. The road slope model was validated against one-meter LIDAR surface elevation profiles. These inference algorithms have been successfully implemented for regions of northern Afghanistan, and some initial results are presented.