icp: icp

In LOMAR: Localization Microscopy Data Analysis

icp

Description

Rigid registration of two point sets using the iterative closest point algorithm.

Usage

icp(
  X,
  Y,
  weights = NULL,
  iterations = 100,
  subsample = NULL,
  scale = FALSE,
  tol = 0.001,
  rotation.only = FALSE
)

Arguments

X	reference point set, a N x D matrix
Y	point set to transform, a M x D matrix,
weights	vector of length nrow(Y) containing weights for each point in Y. Not implemented.
iterations	number of iterations to perform (default: 100)
subsample	can be set either to a number in [0,1] representing the fraction of points to randomly select or a list with vector elements X and Y containing indices of points to select in X and Y
scale	logical (default: FALSE), whether to use scaling.
tol	tolerance for determining convergence
rotation.only	logical (default: FALSE), if TRUE, don't perform translation

Value

a list of

• Y: transformed point set, a M x D matrix,

• R: rotation matrix,

• t: translation vector,

• s: scaling factor,

• iter: number of iterations performed,

• conv: boolean, whether the algorithm has converged.

Examples

data.file1 <- system.file("test_data", "parasaurolophusA.txt", package = "LOMAR",
 mustWork = TRUE)
PS1 <- read.csv(data.file1, sep = '\t', header = FALSE)
data.file2 <- system.file("test_data", "parasaurolophusB.txt", package = "LOMAR",
 mustWork = TRUE)
PS2 <- read.csv(data.file2, sep = '\t', header = FALSE)
transformation <- icp(PS1, PS2, iterations = 10, tol = 1e-3)
## Not run: 
# Visualize registration outcome
library(rgl)
plot3d(PS1, col = "blue")
points3d(PS2, col = "green")
points3d(transformation[['Y']], col = "magenta")

## End(Not run)

LOMAR documentation built on Feb. 5, 2026, 5:08 p.m.