This paper presents a method for tracking a 3D textureless object which undergoes elastic deformations, using the point cloud data provided by an RGB-D sensor and in real-time. This solution is expected to be useful for enhanced manipulation of humanoid robotic systems, especially in the case of pizza dough to be ideally manipulated by a pizza chef robot. Our tracking framework relies on a prior visual segmentation of the object in the image. The segmented point cloud is registered first in a rigid manner and then by non-rigidly fitting the mesh, based on the Finite Element Method to model elasticity, and on geometrical point-to-point correspondences to compute external forces exerted on the mesh. The system has been evaluated on synthetic and real data, and by integrating it into manipulation experiments on the RoDyMan1 http://www.rodyman.eu/. The research leading to these results has been supported by the RoDyMan project, which has received funding from the European Research Council(FP7 IDEAS) under Advanced Grant agreement number 320992. The authors are solely responsible for its content. It does not represent the opinion of the European Community and the Community is not responsible for any use that might be made of the information contained therein. humanoid robotic platform.

Tracking elastic deformable objects with an RGB-D sensor for a pizza chef robot / Petit, Antoine; Lippiello, Vincenzo; Fontanelli, GIUSEPPE ANDREA; Siciliano, Bruno. - In: ROBOTICS AND AUTONOMOUS SYSTEMS. - ISSN 0921-8890. - 88:(2017), pp. 187-201. [10.1016/j.robot.2016.08.023]

Tracking elastic deformable objects with an RGB-D sensor for a pizza chef robot

LIPPIELLO, VINCENZO;FONTANELLI, GIUSEPPE ANDREA;SICILIANO, BRUNO
2017

Abstract

This paper presents a method for tracking a 3D textureless object which undergoes elastic deformations, using the point cloud data provided by an RGB-D sensor and in real-time. This solution is expected to be useful for enhanced manipulation of humanoid robotic systems, especially in the case of pizza dough to be ideally manipulated by a pizza chef robot. Our tracking framework relies on a prior visual segmentation of the object in the image. The segmented point cloud is registered first in a rigid manner and then by non-rigidly fitting the mesh, based on the Finite Element Method to model elasticity, and on geometrical point-to-point correspondences to compute external forces exerted on the mesh. The system has been evaluated on synthetic and real data, and by integrating it into manipulation experiments on the RoDyMan1 http://www.rodyman.eu/. The research leading to these results has been supported by the RoDyMan project, which has received funding from the European Research Council(FP7 IDEAS) under Advanced Grant agreement number 320992. The authors are solely responsible for its content. It does not represent the opinion of the European Community and the Community is not responsible for any use that might be made of the information contained therein. humanoid robotic platform.
2017
Tracking elastic deformable objects with an RGB-D sensor for a pizza chef robot / Petit, Antoine; Lippiello, Vincenzo; Fontanelli, GIUSEPPE ANDREA; Siciliano, Bruno. - In: ROBOTICS AND AUTONOMOUS SYSTEMS. - ISSN 0921-8890. - 88:(2017), pp. 187-201. [10.1016/j.robot.2016.08.023]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/649348
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