Combining top-down spatial reasoning and bottom-up object class recognition for scene understanding

Lars Kunze*, Chris Burbridge, Marina Alberti, Akshaya Tippur, John Folkesson, Patric Jensfelt, Nick Hawes

*Corresponding author for this work

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

25 Citations (Scopus)

Abstract

Many robot perception systems are built to only consider intrinsic object features to recognise the class of an object. By integrating both top-down spatial relational reasoning and bottom-up object class recognition the overall performance of a perception system can be improved. In this paper we present a unified framework that combines a 3D object class recognition system with learned, spatial models of object relations. In robot experiments we show that our combined approach improves the classification results on real world office desks compared to pure bottom-up perception. Hence, by using spatial knowledge during object class recognition perception becomes more efficient and robust and robots can understand scenes more effectively.

Original languageEnglish
Title of host publicationIEEE International Conference on Intelligent Robots and Systems
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages2910-2915
Number of pages6
ISBN (Print)9781479969340
DOIs
Publication statusPublished - 31 Oct 2014
Event2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2014 - Chicago, United States
Duration: 14 Sept 201418 Sept 2014

Conference

Conference2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2014
Country/TerritoryUnited States
CityChicago
Period14/09/1418/09/14

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Computer Vision and Pattern Recognition
  • Computer Science Applications

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