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. Author manuscript; available in PMC: 2015 Jul 28.
Published in final edited form as: IEEE Int Conf Robot Autom. 2014 May 31;2014:4368–4373. doi: 10.1109/ICRA.2014.6907495

Algorithm 1.

PCA using SVD

for i = 0 : N do
  P3D(i, 1) ← tip_position_x(i)
  P3D(i, 2) ← tip_position_y(i)
  P3D(i, 3) ← tip_position_z(i)
end for
3DP3D−mean(P3D) {subtract the mean value}
[U, Σ, V] ←svd(3D) {SVD decomposition}
P2DUT P3D {project the 3D data}
d ← circlefitting(P2D) {Least squares circle fitting}
κ̂d ← 1/d