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Recognition and localization of overlapping parts from sparse data

Author: William Eric Leifur Grimson; Tomás Lozano-Pérez; Massachusetts Institute of Technology. Artificial Intelligence Laboratory.
Publisher: Cambridge, Mass. : Massachusetts Institute of Technology, Artificial Intelligence Laboratory, 1985.
Series: A.I. memo, 841.
Edition/Format:   Book : English
Database:WorldCat
Summary:
Abstract: "This paper discusses how sparse local measurements of positions and surface normals may be used to identify and locate overlapping objects. The objects are modeled as polyhedra (or polygons) having up to six degrees of positional freedom relative to the sensors. The approach operates by examining all hypotheses about pairings between sensed data object surfaces and efficiently discarding inconsistent ones
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Document Type: Book
All Authors / Contributors: William Eric Leifur Grimson; Tomás Lozano-Pérez; Massachusetts Institute of Technology. Artificial Intelligence Laboratory.
OCLC Number: 20781564
Notes: "June, 1985."
Cover title.
Description: 40 p. : ill. ; 28 cm.
Series Title: A.I. memo, 841.
Responsibility: W. Eric L. Grimson, Tomás Lozano-Pérez.

Abstract:

Abstract: "This paper discusses how sparse local measurements of positions and surface normals may be used to identify and locate overlapping objects. The objects are modeled as polyhedra (or polygons) having up to six degrees of positional freedom relative to the sensors. The approach operates by examining all hypotheses about pairings between sensed data object surfaces and efficiently discarding inconsistent ones by using local constraints on: distances between faces, angles between face normals, and angles (relative to the surface normals) of vectors between sensed points.

The method described here is an extension of a method for recognition and localization of non-overlapping parts previously described in [Grimson and Lozano-Pérez 84] and [Gaston and Lozano-Pérez 84]."

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