Paper Info
Title
Theory and Algorithms for Weighted Total Least-Squares Fitting of Lines, Planes, and Parallel Planes to Support Tolerancing Standards
Abstract
We present the theory and algorithms for fitting a line, a plane, two parallel planes (corresponding to a slot or a slab), or many parallel planes in a total (orthogonal) least-squares sense to coordinate data that is weighted. Each of these problems is reduced to a simple 3 x 3 matrix eigenvalue/eigenvector problem or an equivalent singular value decomposition problem, which can be solved using reliable and readily available commercial software. These methods were numerically verified by comparing them with brute-force minimization searches. We demonstrate the need for such weighted total least-squares fitting in coordinate metrology to support new and emerging tolerancing standards, for instance, ISO 14405-1:2010. The widespread practice of unweighted fitting works well enough when point sampling is controlled and can be made uniform (e. g., using a discrete point contact coordinate measuring machine). However, we show by example that non-uniformly sampled points (arising from many new measurement technologies) coupled with unweighted least-squares fitting can lead to erroneous results. When needed, the algorithms presented also solve the unweighted cases simply by assigning the value one to each weight. We additionally prove convergence from the discrete to continuous cases of least-squares fitting as the point sampling becomes dense.
Year
DOI
Venue
2013
10.1115/1.4024854
JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING
Keywords
DocType
Volume
eigenvalues,algorithms
Journal
13
Issue
ISSN
Citations 
3
1530-9827
4
PageRank 
References 
Authors
0.72
2
2
Name
Order
Citations
PageRank
craig m shakarji1101.95
Vijay Srinivasan211613.20