High resolution terrain mapping using low attitude aerial stereo imagery

This paper presents an approach to build high resolution digital elevation maps from a sequence of unregistered low altitude stereovision image pairs. The approach first uses a visual motion estimation algorithm that determines the 3D motions of the cameras between consecutive acquisitions, on the basis of visually detected and matched environment features. An extended Kalman filter then estimates both the 6 position parameters and the 3D positions of the memorized features as images are acquired. Details are given on the filter implementation and on the estimation of the uncertainties on the feature observations and motion estimations. Experimental results show that the precision of the method enables to build spatially consistent very large maps.