Learning Landmarks for Robot Localization

Robert Sim and Gregory Dudek, McGill University

Our work addresses the problem of learning a set of visual landmarks for mobile robot localization. The learning framework is designed to be applicable to a wide range of environments, and allows for different approaches to computing a pose estimate. Initially, each landmark is detected using a model of visual attention and is matched to observations from other poses using principal components analysis. Attributes of the observed landmarks can be parameterized using a generic parameterization method and then evaluated in terms of their utility for pose estimation. We discuss the status of the work to date, and future directions.

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