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

Louis-Charles Caron 1, 2 Yang Song 1 David Filliat 1, 2 Alexander Gepperth 1, 2
2 Flowers - Flowing Epigenetic Robots and Systems
Inria Bordeaux - Sud-Ouest, U2IS - Unité d'Informatique et d'Ingénierie des Systèmes
Abstract : We present a neural network based fusion approach for real- time 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.
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Louis-Charles Caron, Yang Song, David Filliat, Alexander Gepperth. Neural network based 2D/3D fusion for robotic object recognition. European Symposium on artificial neural networks (ESANN), May 2014, Bruges, Belgium. pp.127 - 132, 2014. 〈hal-01012090〉

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