We have been developing an architecture for vision-based navigation which relies on continuous feedback from visual "landmarks" to control robot motion, In this approach, landmarks are consistently located and acquired as they come into view. To make this process efficient and robust, it is important that the image locations of these features can be predicted from available image information. In this article, we discuss methods for direct image-based prediction of point and line features for a mobile system operating on a planar surface. Preliminary experimental results suggest that image-based prediction con be performed efficiently and with sufficient accuracy to ensure robust acquisition of navigational 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.