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Recognition and Localization of Overlapping Parts from Sparse Data

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 degreed of positional freedom relative to the sensors. The approach operated by examining all hypotheses about pairings between sensed data and 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-Perez 84] and [Gaston and Lozano-Perez 84].

Identiferoai:union.ndltd.org:MIT/oai:dspace.mit.edu:1721.1/5611
Date01 June 1985
CreatorsGrimson, W. Eric L., Lozano-Perez, Tomas
Source SetsM.I.T. Theses and Dissertation
Languageen_US
Detected LanguageEnglish
Format41 p., 8294299 bytes, 6516094 bytes, application/postscript, application/pdf
RelationAIM-841

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