Map Learning with Indistinguishable Locations

TL;DR

This paper demonstrates that large-scale spatial map construction can be efficiently achieved despite uncertainties from sensor inaccuracies and location recognition errors, addressing key challenges in navigation systems.

Contribution

It introduces methods to effectively handle both directional and recognition uncertainties in large-scale spatial reasoning for map learning.

Findings

Efficient algorithms for map construction under uncertainty

Handling recognition errors improves map accuracy

Applicable to large-scale navigation systems

Abstract

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.