Map Learning with Indistinguishable Locations
  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.
