Model-Based Recognition and Localization from Sparse Range or Tactile Data

Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/6395

Title:	Model-Based Recognition and Localization from Sparse Range or Tactile Data
Issue Date:	9-Oct-2013
Description:	This paper discusses how local measurements of three-dimensional positions and surface normals (recorded by a set of tactile sensors, or by three-dimensional range sensors), may be used to identify and locate objects, from among a set of known objects. The objects are modeled as polyhedra having up to six degrees of freedom relative to the sensors. We show that inconsistent hypotheses about pairings between sensed points and object surfaces can be discarded efficiently by using local constraints on: distances between faces, angles between face normals, and angles (relative to the surface normals) of vectors between sensed points. We show by simulation and by mathematical bounds that the number of hypotheses consistent with these constraints is small. We also show how to recover the position and orientation of the object from the sense data. The algorithm's performance on data obtained from a triangulation range sensor is illustrated.
URI:	http://koha.mediu.edu.my:8181/xmlui/handle/1721
Other Identifiers:	AIM-738 http://hdl.handle.net/1721.1/6395
Appears in Collections:	MIT Items

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