This function is meant to be used inside
stemSegmentation. It applies a reweighted least squares circle fit algorithm using M-estimators in order to remove outlier effects.
sgt.irls.circle(tol = 0.1, n = 500)
cylinder estimation methods
perform automatic outlier assigning through iterative reweighting
with M-estimators, followed by a Nelder-Mead optimization of squared distance sums
to determine the best circle/cylinder parameters for a given point
cloud. The reweighting strategy used in TreeLS is based on
Liang et al. (2012). The Nelder-Mead algorithm implemented in Rcpp was provided by
The circle fit methods applied in TreeLS estimate the circle parameters (its center's XY coordinates and radius) from a pre-selected (denoised) set of points in a least squares fashion by applying either QR decompostion, used in combination with the RANSAC algorithm, or Nelder-Mead simplex optimization combined the IRLS approach.
The parameters returned by the circle fit methods are:
X,Y: 2D circle center coordinates
Radius: 2D circle radius, in point cloud units
Error: model circle error from the least squares fit
AvgHeight: average height of the stem segment's points
N: number of points belonging to the stem segment
Liang, X. et al., 2012. Automatic stem mapping using single-scan terrestrial laser scanning. IEEE Transactions on Geoscience and Remote Sensing, 50(2), pp.661–670.
Conto, T. et al., 2017. Performance of stem denoising and stem modelling algorithms on single tree point clouds from terrestrial laser scanning. Computers and Electronics in Agriculture, v. 143, p. 165-176.
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