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)numeric - tolerance offset between absolute radii estimates and hough transform estimates.
numeric - maximum number of points to sample for fitting stem segments.
irls circle or 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
kthohr/optim.
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<U+2013>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.