Bootstrapping probabilistic models of qualitative spatial relations for active visual object search

Lars Kunze, Chris Burbridge, Nick Hawes

Research output: Chapter in Book/Report/Conference proceedingConference contribution

14 Citations (Scopus)

Abstract

In many real world applications, autonomous mobile robots are required to observe or retrieve objects in their environment, despite not having accurate estimates of the objects' locations. Finding objects in real-world settings is a non-trivial task, given the complexity and the dynamics of human environments. However, by understanding and exploiting the structure of such environments, e.g. where objects are commonly placed as part of everyday activities, robots can perform search tasks more efficiently and effectively than without such knowledge. In this paper we investigate how probabilistic models of qualitative spatial relations can improve the performance in object search tasks. Specifically, we learn Gaussian Mixture Models of spatial relations between object classes from descriptive statistics of real office environments. Experimental results with a range of sensor models suggest that our model improves overall performance in object search tasks.

Original languageEnglish
Title of host publicationAAAI Spring Symposium - Technical Report
PublisherAI Access Foundation
Pages81-88
Number of pages8
VolumeSS-14-06
ISBN (Print)9781577356462
Publication statusPublished - 2014
Event2014 AAAI Spring Symposium - Palo Alto, CA, United States
Duration: 24 Mar 201426 Mar 2014

Conference

Conference2014 AAAI Spring Symposium
Country/TerritoryUnited States
CityPalo Alto, CA
Period24/03/1426/03/14

ASJC Scopus subject areas

  • Artificial Intelligence

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