Learning generative models of invariant features

We present a method for learning a set of models of visual features which are invariant to scale and translation in the image domain. The models are constructed by first applying the scale-invariant feature transform (SIFT) to a set of training images, and matching the extracted features across the images, followed by learning the pose-dependent behavior of the features. The modeling process avoids assumptions with respect to scene and imaging geometry, but rather learns the direct mapping from camera pose to feature observation. Such models are useful for applications to robotic tasks, such as localization, as well as visualization tasks. We present the model learning framework, and experimental results illustrating the success of the method for learning models that are useful for robot localization.