circleRANSAC: Adaptive RANSAC Circle Fitting
In rTLS: Processing of Point Clouds Derived from Terrestrial Laser Scanning

circleRANSAC

R Documentation

Adaptive RANSAC Circle Fitting

Description

Adaptive random sample consensus for cicle fitting.

Usage

circleRANSAC(
  cloud,
  fpoints,
  pconf,
  poutlier,
  max_iterations,
  threads = 1L,
  plot = TRUE
)

Arguments

`cloud`	A `data.table` with XY coordinates in the first two columns.
`fpoints`	A `numeric` vector between 0 and 1 representing the fraction of point samples that will be used during each iteration.
`pconf`	A `numeric` vector between 0 and 1 describing the confidence threshold to consider a point in a given fitted circle outlier or inlier.
`poutlier`	A `numeric` vector of length two describing the proportion of outliers to consider inside or outsite of the `pconf` threshold.
`max_iterations`	An `integer` specifying the number of iterations. If `NULL`, the number of iterations are automaticaly estimated using `pconf`, `1 - poutlier`, and `1 - fpoints`; see details.
`threads`	An `integer` specifying the number of threads to use for parallel processing. Experiment to see what works best for your data on your hardware.
`plot`	Logical. If `TRUE`, it provides visual representation of the fitted circle.

Value

A data.table with the *XY* coordinate information of the circle center, the radius, the error based on the least squares fit, and the proportion of inliers.

Author(s)

J. Antonio Guzmán Q.

Examples


#Point cloud
data("pc_tree")

#Subset region at at breast height
sub <- pc_tree[between(Z, 1.25, 1.35),]

#Fit circle
circleRANSAC(sub, fpoints = 0.2, pconf = 0.95, poutlier = c(0.5, 0.5), max_iterations = 100)

rTLS documentation built on Dec. 28, 2022, 1:28 a.m.