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An experimental study of four variants of pose clustering from dense range data

Hillenbrand, Ulrich und Fuchs, Alexander (2011) An experimental study of four variants of pose clustering from dense range data. Computer Vision and Image Understanding, Seiten 1427-1448. DOI: 10.1016/j.cviu.2011.06.007. ISSN 1077-3142.

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Offizielle URL: http://www.sciencedirect.com/science/article/pii/S1077314211001445

Kurzfassung

Parameter clustering is a robust estimation technique based on location statistics in a parameter space where parameter samples are computed from data samples. This article investigates parameter clustering as a global estimator of object pose or rigid motion from dense range data without knowing correspondences between data points. Four variants of the algorithm are quantitatively compared regarding estimation accuracy and robustness: sampling poses from data points or from points with surface normals derived from them, each combined with clustering poses in the canonical or consistent parameter space, as defined in Hillenbrand (2007). An extensive test data set is employed: synthetic data generated from a public database of three-dimensional object models through various levels of corruption of their geometric representation; real range data from a public database of models and cluttered scenes. It turns out that sampling raw data points and clustering in the consistent parameter space yields the estimator most robust to data corruption. For data of sufficient quality, however, sampling points with normals is more efficient; this is most evident when detecting objects in cluttered scenes. Moreover, the consistent parameter space is always preferable to the canonical parameter space for clustering.

Dokumentart:Zeitschriftenbeitrag
Titel:An experimental study of four variants of pose clustering from dense range data
Autoren:
AutorenInstitution oder E-Mail-Adresse der Autoren
Hillenbrand, UlrichInstitute of Robotics and Mechatronics, German Aerospace Center (DLR)
Fuchs, AlexanderInstitute of Robotics and Mechatronics, German Aerospace Center (DLR)
Datum:Oktober 2011
Erschienen in:Computer Vision and Image Understanding
Referierte Publikation:Ja
In SCOPUS:Ja
In ISI Web of Science:Ja
DOI :10.1016/j.cviu.2011.06.007
Seitenbereich:Seiten 1427-1448
ISSN:1077-3142
Status:veröffentlicht
Stichwörter:robust estimation; pose estimation; range data; parameter density; clustering; performance evaluation
HGF - Forschungsbereich:Luftfahrt, Raumfahrt und Verkehr
HGF - Programm:Raumfahrt
HGF - Programmthema:Technik für Raumfahrt
DLR - Schwerpunkt:Raumfahrt
DLR - Forschungsgebiet:R SY - Technik für Raumfahrtsysteme
DLR - Teilgebiet (Projekt, Vorhaben):R - RMC - Kognitive Intelligenz und Autonomie (alt)
Standort: Oberpfaffenhofen
Institute & Einrichtungen:Institut für Robotik und Mechatronik > Robotersysteme
Hinterlegt von: Ulrich Hillenbrand
Hinterlegt am:09 Jan 2012 11:44
Letzte Änderung:26 Feb 2013 15:03

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