Neural network based 2D/3D fusion for robotic object recognition

  • Louis Charles Caron
  • , Yang Song
  • , David Filliat
  • , Alexander Gepperth

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

Abstract

We present a neural network based fusion approach for realtime robotic object recognition which integrates 2D and 3D descriptors in a flexible way. The presented recognition architecture is coupled to a real-time segmentation step based on 3D data, since a focus of our investigations is real-world operation on a mobile robot. As recognition must operate on imperfect segmentation results, we conduct tests of recognition performance using complex everyday objects in order to quantify the overall gain of performing 2D/3D fusion, and to discover where it is particularly useful. We find that the fusion approach is most powerful when generalization is required, for example to significant viewpoint changes and a large number of object categories, and that a perfect segmentation is apparently not a necessary prerequisite for successful discrimination.

Original languageEnglish
Title of host publication22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2014 - Proceedings
Publisheri6doc.com publication
Pages431-436
Number of pages6
ISBN (Electronic)9782874190957
Publication statusPublished - 1 Jan 2014
Event22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2014 - Bruges, Belgium
Duration: 23 Apr 201425 Apr 2014

Publication series

Name22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2014 - Proceedings

Conference

Conference22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2014
Country/TerritoryBelgium
CityBruges
Period23/04/1425/04/14

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