Estimating fractal dimension of natural terrain from irregularly spaced data

Abstract

The authors propose a method to estimate terrain roughness directly from the depth information. They estimate the fractal dimension of terrain using the fractal Brownian function approach. For experiments with real data, they extend the approach to accommodate irreguarly sampled elevation data supplied by a scanning laser rangefinder. Applying this extended method to noisy range imagery of natural terrain (sand and rocks), the authors find that the resulting estimates of fractal dimension correlate closely to human perception of the roughness of the terrain, showing that the fractal dimension of the sensed point set is a practical and effective measure of the roughness of natural terrain.