Learning environmental features for pose estimation

Abstract We present a method for learning a set of environmental features which are useful for pose estimation. The landmark learning mechanism is designed to be applicable to a wide range of environments, and generalized for different sensing modalities. In the context of computer vision, each landmark is detected as a local extremum of a measure of distinctiveness and represented by an appearance-based encoding which is exploited for matching. The set of obtained landmarks can be parameterized and then evaluated in terms of their utility for the task at hand. The method is used to motivate a general approach to task-oriented sensor fusion. We present experimental evidence that demonstrates the utility of the method.

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