Surface landmark selection and matching in natural terrain

In this paper we present an algorithm for robust absolute position estimation in natural terrain based on landmarks extracted from dense 3-D surfaces. Our landmarks are constructed by concatenating pose dependent oriented surface points with pose invariant surface signatures into a single feature vector; this definition of landmarks allows a priori pose information to be used to constrain the search for landmark matches. The first step in our algorithm is to extract landmarks from stable and salient surface patches. These landmarks are then stored in a closest point search structure with which landmarks are matched efficiently using available pose constraints and invariant values. Finally, an iterative pose estimation algorithm, based on least median squares, is wrapped around landmark matching to eliminate outliers and estimate absolute position. To validate our algorithm, we show hundreds of absolute position estimation results from three different natural scenes. These results show that our algorithm can incorporate constraints on position and attitude for efficient landmark matching and match small and dense scene surface patches to large and coarse model surfaces.