icp {LOMAR}

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
)

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

if set, use this randomly selected fraction of the points

scale

logical (default: FALSE), whether to use scaling.

tol

tolerance for determining convergence

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)