sgt.irls.circle: Stem segmentation algorithm: Iterated Reweighted Least...
In TreeLS: Terrestrial Point Cloud Processing of Forest Data

Description Usage Arguments Iterative Reweighted Least Squares (IRLS) Algorithm Least Squares Circle Fit References

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.

1	sgt.irls.circle(tol = 0.1, n = 500)

`tol`	`numeric` - tolerance offset between absolute radii estimates and hough transform estimates.
`n`	`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.661–670.

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.

TreeLS documentation built on Aug. 26, 2020, 5:14 p.m.