A probabilistic spatial data model

Yoram Kornatzky, Solomon Eyal Shimony

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Spatial information in autonomous robot tasks is uncertain due to measurement errors, the dynamic nature of the world, and an incompletely known environment. We present a probabilistic spatial data model capable of describing relevant spatial data, such as object location, shape, composition, and other parameters, in the presence of uncertainty. Uncertain spatial information is modeled through continuous probability distributions on values of attributes. The data model is designed to support our visual tracking and navigation prototype.

Original languageEnglish
Pages (from-to)51-74
Number of pages24
JournalInformation Sciences
Issue number1-4
StatePublished - 1 Jan 1996

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Theoretical Computer Science
  • Computer Science Applications
  • Information Systems and Management
  • Artificial Intelligence


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