Map Learning with Indistinguishable Locations
This addresses the challenge of building reliable maps for navigation systems in the presence of common uncertainties, though it appears incremental as it focuses on a specific class of problems rather than a broad breakthrough.
The paper tackles the problem of constructing spatial representations (maps) for navigation when there is directional uncertainty from sensor inaccuracies and recognition uncertainty from mistaken location identification, showing that a particular class of these spatial reasoning problems can be solved efficiently despite both types of uncertainty.
Nearly all spatial reasoning problems involve uncertainty of one sort or another. Uncertainty arises due to the inaccuracies of sensors used in measuring distances and angles. We refer to this as directional uncertainty. Uncertainty also arises in combining spatial information when one location is mistakenly identified with another. We refer to this as recognition uncertainty. Most problems in constructing spatial representations (maps) for the purpose of navigation involve both directional and recognition uncertainty. In this paper, we show that a particular class of spatial reasoning problems involving the construction of representations of large-scale space can be solved efficiently even in the presence of directional and recognition uncertainty. We pay particular attention to the problems that arise due to recognition uncertainty.