Analysis of Tolerance for Manufacturing Geometric Objects from Sense Data
نویسندگان
چکیده
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. 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. Analysis of Tolerance for Manufacturing Geometric Objects from Sense Data Tarek M. Sobh, Xiao Hong Zhu, and Beat Briiderlin * Department of Computer Science University of Utah Salt Lake City, Utah 84112
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