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

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

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

Abstract

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.

Document Type:Article
Title:An experimental study of four variants of pose clustering from dense range data
Authors:
AuthorsInstitution or Email of Authors
Hillenbrand, UlrichInstitute of Robotics and Mechatronics, German Aerospace Center (DLR)
Fuchs, AlexanderInstitute of Robotics and Mechatronics, German Aerospace Center (DLR)
Date:October 2011
Journal or Publication Title:Computer Vision and Image Understanding
Refereed publication:Yes
In SCOPUS:Yes
In ISI Web of Science:Yes
DOI:10.1016/j.cviu.2011.06.007
Page Range:pp. 1427-1448
ISSN:1077-3142
Status:Published
Keywords:robust estimation; pose estimation; range data; parameter density; clustering; performance evaluation
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Space Technology
DLR - Research area:Raumfahrt
DLR - Program:R SY - Technik für Raumfahrtsysteme
DLR - Research theme (Project):R - RMC - Kognitive Intelligenz und Autonomie (old)
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Robotics and Mechatronics > Robotic Systems
Deposited By: Ulrich Hillenbrand
Deposited On:09 Jan 2012 11:44
Last Modified:26 Feb 2013 15:03

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