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)

1 RV - Robotique et Vision
2 Flowers - Flowing Epigenetic Robots and Systems

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.

Domaines: Robotique [cs.RO]

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. ⟨hal-01012090⟩