Local compliance estimation via positive semidefinite constrained least squares

被引：11
|
作者：
Krislock, N [1 ]
Lang, JC
Varah, J
Pai, DK
Seidel, HP
机构：
[1] Univ British Columbia, Inst Appl Math, Vancouver, BC V6T 1Z2, Canada
[2] MPI Format, D-66123 Saarbrucken, Germany
[3] Univ British Columbia, Dept Comp Sci, Vancouver, BC V6T 1Z4, Canada
[4] Rutgers State Univ, Div Comp & Informat Sci, Piscataway, NJ 08854 USA
来源：
IEEE TRANSACTIONS ON ROBOTICS AND AUTOMATION | 2004年 / 20卷 / 06期
基金：
美国国家科学基金会;
关键词：
constrained linear least squares; deformable models; stiffness matrix;
D O I：
10.1109/TRO.2004.832794
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
We present a method to estimate a positive semidefinite matrix by linear least squares, and we apply this method to the estimation of local compliance matrices during deformable object modeling. Estimation of physical quantities from measurements has to consider noise due to measurement and modeling inaccuracy. Enforcing constraints during estimation can guarantee physically plausible results even under difficult measurement conditions.
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页码：1007 / 1011
页数：5
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