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Enhancing Visual Perception of Shape through Tactile Glances
KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.ORCID-id: 0000-0003-0579-3372
KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.ORCID-id: 0000-0003-2965-2953
2013 (engelsk)Inngår i: Intelligent Robots and Systems (IROS), 2013 IEEE/RSJ International Conference on, IEEE conference proceedings, 2013, s. 3180-3186Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Object shape information is an important parameter in robot grasping tasks. However, it may be difficult to obtain accurate models of novel objects due to incomplete and noisy sensory measurements. In addition, object shape may change due to frequent interaction with the object (cereal boxes, etc). In this paper, we present a probabilistic approach for learning object models based on visual and tactile perception through physical interaction with an object. Our robot explores unknown objects by touching them strategically at parts that are uncertain in terms of shape. The robot starts by using only visual features to form an initial hypothesis about the object shape, then gradually adds tactile measurements to refine the object model. Our experiments involve ten objects of varying shapes and sizes in a real setup. The results show that our method is capable of choosing a small number of touches to construct object models similar to real object shapes and to determine similarities among acquired models.

sted, utgiver, år, opplag, sider
IEEE conference proceedings, 2013. s. 3180-3186
Serie
IEEE International Conference on Intelligent Robots and Systems, ISSN 2153-0858
Emneord [en]
Initial hypothesis, Learning object model, Physical interactions, Probabilistic approaches, Sensory measurement, Tactile perception, Unknown objects, Visual perception
HSV kategori
Identifikatorer
URN: urn:nbn:se:kth:diva-133212DOI: 10.1109/IROS.2013.6696808ISI: 000331367403037Scopus ID: 2-s2.0-84891043014ISBN: 978-1-4673-6358-7 (tryckt)OAI: oai:DiVA.org:kth-133212DiVA, id: diva2:659977
Konferanse
2013 26th IEEE/RSJ International Conference on Intelligent Robots and Systems: New Horizon, IROS 2013; Tokyo; Japan; 3 November 2013 through 8 November 2013
Merknad

QC 20131104

Tilgjengelig fra: 2013-10-28 Laget: 2013-10-28 Sist oppdatert: 2018-01-11bibliografisk kontrollert

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2013_IROS_bbhk.pdf(5773 kB)330 nedlastinger
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