Toward selecting and recognizing natural landmarks

Landmarks are often used as a basis for mobile robot navigation. In this paper, we consider the problem of automatically selecting from a set of 3D features the subset which is most likely to be recognized from noisy monocular image data and is least likely to be confused with any other group of features. Assuming perspective projection, real valued recognition functions are constructed for a set of features. The value returned from such functions are invariant to changes of viewpoint and can be evaluated directly from image measurements without prior knowledge of the position and orientation of the camera. With image noise, the recognition function no longer evaluates to a constant value. Because of the possibility of false matches, a Bayes detector is used to determine the optimal range of values of the recognition function that will be accepted as image features of the model. The model with the lowest Bayes cost is selected as the most distinguishable landmark. We show implementation results for real 3D objects