Analysis of Tolerance for Manufacturing Geometric Objects from Sense Data


	
	      Tarek M. Sobh, Xiao Hong Zhu, and Beat Bruderlin


 
                                UUCS-93-025

                               October, 1993


                       Department of Computer Science
                              University of Utah
                       Salt Lake City, UT 84112 USA



                                  Abstract


	In this work we address the problem of manufacturing machine
	parts from sense data. Constructing geometric models for the
	objects from sense data is the intermediate step in a reverse
	engineering manufacturing system. Sensors are usually inaccurate,
	providing uncertain sense information. We construct geometric entities
	with uncertainty models for the objects under consideration from
	noisy measurements and proceed to do reasoning on the uncertain
	geometries, thus, adding robustness to the construction of
	geometries from sensed data.



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This work was supported in part by ARPA under ARO grant number 
DAAH04-93-G-0420, NSF grant CDA 9024721, and a University of Utah 
Research Committee grant.  All opinions, findings, conclusions or 
recommendations expressed in this document are those of the author
and do not necessarily reflect the views of the sponsoring agencies.