Image-Directed Sampling for Geometric Modeling of Lunar Terrain

Abstract

Geometric modeling from range scanners can be vastly improved by sampling the scene with a Nyquist criterion. This work presents a method to estimate frequency content a priori from intensity imagery using wavelet analysis and to utilize these estimates in efficient single-view sampling. The key idea is that under certain constrained and estimable image formation conditions, images are a strong predictor of surface frequency. This approach is explored in the context of lunar application to enhance robotic modeling. Experimentation on simulated data and in artificial lunar terrain at aerial and ground rover scales is documented. Results show up to 40 % improvement in MSE reconstruction error. Lastly, a class of image-directed range sensors is described and a hardware implementation of this paradigm on a structured light scanner is demonstrated.