Estimating Uncertain Spatial Relationships in Robotics
Randall Smith, Matthew Self, Peter Cheeseman
In this paper, we describe a representation for spatial information, called the stochastic map, and associated procedures for building it, reading information from it, and revising it incrementally as new information is obtained. The map contains the estimates of relationships among objects in the map, and their uncertainties, given all the available information. The procedures provide a general solution to the problem of estimating uncertain relative spatial relationships. The estimates are probabilistic in nature, an advance over the previous, very conservative, worst-case approaches to the problem. Finally, the procedures are developed in the context of state-estimation and filtering theory, which provides a solid basis for numerous extensions.
Keywords: Stochastic Map, Spatial Information, Relative Spatial Relationships
PDF Link: /papers/86/p435-smith.pdf
AUTHOR = "Randall Smith
and Matthew Self and Peter Cheeseman",
TITLE = "Estimating Uncertain Spatial Relationships in Robotics",
BOOKTITLE = "Uncertainty in Artificial Intelligence 2 Annual Conference on Uncertainty in Artificial Intelligence (UAI-86)",
PUBLISHER = "Elsevier Science",
ADDRESS = "Amsterdam, NL",
YEAR = "1986",
PAGES = "435--461"