A probabilistic spatial data model

Yoram Kornatzky; Solomon Eyal Shimony

doi:10.1016/0020-0255(95)00241-3

A probabilistic spatial data model

Yoram Kornatzky, Solomon Eyal Shimony

Department of Computer Science

Research output: Contribution to journal › Article › peer-review

2 Scopus citations

Abstract

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 language	English
Pages (from-to)	51-74
Number of pages	24
Journal	Information Sciences
Volume	90
Issue number	1-4
DOIs	https://doi.org/10.1016/0020-0255(95)00241-3
State	Published - 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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10.1016/0020-0255(95)00241-3

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title = "A probabilistic spatial data model",

abstract = "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.",

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AB - 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.

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U2 - 10.1016/0020-0255(95)00241-3

DO - 10.1016/0020-0255(95)00241-3

M3 - Article

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VL - 90

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JO - Information Sciences

JF - Information Sciences

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A probabilistic spatial data model

Abstract

ASJC Scopus subject areas

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