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Sensors 2017, 17(5), 1083; doi:10.3390/s17051083

Acquisition and Neural Network Prediction of 3D Deformable Object Shape Using a Kinect and a Force-Torque Sensor

Department of Computer Science and Engineering, Université du Québec en Outaouais, Gatineau, J8X 3X7 QC, Canada
This paper is an extended version our paper published in Tawbe, B.; Cretu, A.-M. Data-Driven Representation of Soft Deformable Objects Based on Force-Torque Data and 3D Vision Measurements. In Proceedings of the 3rd International Electronic Conference on Sensors and Applications, Online, 15–30 November 2016; Sciforum Electronic Conference Series; Volume 3, p. E006, doi:10.3390/ecsa-3-E006.
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Academic Editor: Stefano Mariani
Received: 23 March 2017 / Revised: 2 May 2017 / Accepted: 5 May 2017 / Published: 11 May 2017
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Abstract

The realistic representation of deformations is still an active area of research, especially for deformable objects whose behavior cannot be simply described in terms of elasticity parameters. This paper proposes a data-driven neural-network-based approach for capturing implicitly and predicting the deformations of an object subject to external forces. Visual data, in the form of 3D point clouds gathered by a Kinect sensor, is collected over an object while forces are exerted by means of the probing tip of a force-torque sensor. A novel approach based on neural gas fitting is proposed to describe the particularities of a deformation over the selectively simplified 3D surface of the object, without requiring knowledge of the object material. An alignment procedure, a distance-based clustering, and inspiration from stratified sampling support this process. The resulting representation is denser in the region of the deformation (an average of 96.6% perceptual similarity with the collected data in the deformed area), while still preserving the object’s overall shape (86% similarity over the entire surface) and only using on average of 40% of the number of vertices in the mesh. A series of feedforward neural networks is then trained to predict the mapping between the force parameters characterizing the interaction with the object and the change in the object shape, as captured by the fitted neural gas nodes. This series of networks allows for the prediction of the deformation of an object when subject to unknown interactions. View Full-Text
Keywords: deformation; force-torque sensor; Kinect; iterative closest point; neural gas; neural networks; clustering; mesh simplification; RGB-D data; 3D object modeling deformation; force-torque sensor; Kinect; iterative closest point; neural gas; neural networks; clustering; mesh simplification; RGB-D data; 3D object modeling
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Tawbe, B.; Cretu, A.-M. Acquisition and Neural Network Prediction of 3D Deformable Object Shape Using a Kinect and a Force-Torque Sensor . Sensors 2017, 17, 1083.

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