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 language | English |
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Title of host publication | IEEE International Conference on Intelligent Robots and Systems |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Pages | 2910-2915 |
Number of pages | 6 |
ISBN (Print) | 9781479969340 |
DOIs | |
Publication status | Published - 31 Oct 2014 |
Event | 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2014 - Chicago, United States Duration: 14 Sept 2014 → 18 Sept 2014 |
Conference
Conference | 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2014 |
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Country/Territory | United States |
City | Chicago |
Period | 14/09/14 → 18/09/14 |
ASJC Scopus subject areas
- Control and Systems Engineering
- Software
- Computer Vision and Pattern Recognition
- Computer Science Applications