2013/03/27 by Randall K. Smith, Randall Smith, Smith, Randall +5 · 1 citation
Computer Science · #AI-based Problem Solving and Planning #Artificial Intelligence (cs.AI) #Constraint Satisfaction and Optimization #FOS: Computer and information sciences #Formal Methods in Verification #cs.AI
paper · pdf · doi:10.48550/arxiv.1304.3111
Appears in Proceedings of the Second Conference on Uncertainty in Artificial Intelligence (UAI1986)
arxiv created 2013/03/27 · openalex publication_date 2013/03/27 · arxiv updated 2013/04/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.