A probabilistic spatial data model

Yoram Kornatzky, Solomon Eyal Shimony

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 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
Title of host publicationDatabase and Expert Systems Applications - 4th International Conference, DEXA 1993, Proceedings
EditorsVladimir Marik, Jiri Lazansky, Roland R. Wagner
PublisherSpringer Verlag
Number of pages12
ISBN (Print)9783540572343
StatePublished - 1 Jan 1993
Event4th International Conference on Database and Expert Systems Applications, DEXA 1993 - Prague, Czech Republic
Duration: 6 Sep 19938 Sep 1993

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume720 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference4th International Conference on Database and Expert Systems Applications, DEXA 1993
Country/TerritoryCzech Republic

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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