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Nearly all spatial reasoning problems involve uncertainty of one sort oranother. Uncertainty arises due to the inaccuracies of sensors used inmeasuring distances and angles. We refer to this as directional uncertainty.Uncertainty also arises in combining spatial information when one location ismistakenly identified with another. We refer to this as recognitionuncertainty. Most problems in constructing spatial representations (maps) forthe purpose of navigation involve both directional and recognition uncertainty.In this paper, we show that a particular class of spatial reasoning problemsinvolving the construction of representations of large-scale space can besolved efficiently even in the presence of directional and recognitionuncertainty. We pay particular attention to the problems that arise due torecognition uncertainty.

Map Learning with Indistinguishable Locations