icp Namespace Reference

Functions
def	best_fit_transform (A, B)
def	icp (A, B, init_pose=None, max_iterations=20, tolerance=0.001)
def	nearest_neighbor (src, dst)

Function Documentation

best_fit_transform()

def icp.best_fit_transform	(		A,
			B
	)

Calculates the least-squares best-fit transform that maps corresponding points A to B in m spatial dimensions
Input:
  A: Nxm numpy array of corresponding points
  B: Nxm numpy array of corresponding points
Returns:
  T: (m+1)x(m+1) homogeneous transformation matrix that maps A on to B
  R: mxm rotation matrix
  t: mx1 translation vector

Definition at line 4 of file icp.py.

icp()

def icp.icp	(		A,
			B,
			init_pose = None,
			max_iterations = 20,
			tolerance = 0.001
	)

The Iterative Closest Point method: finds best-fit transform that maps points A on to points B
Input:
    A: Nxm numpy array of source mD points
    B: Nxm numpy array of destination mD point
    init_pose: (m+1)x(m+1) homogeneous transformation
    max_iterations: exit algorithm after max_iterations
    tolerance: convergence criteria
Output:
    T: final homogeneous transformation that maps A on to B
    distances: Euclidean distances (errors) of the nearest neighbor
    i: number of iterations to converge

Definition at line 67 of file icp.py.

nearest_neighbor()

def icp.nearest_neighbor	(		src,
			dst
	)

Find the nearest (Euclidean) neighbor in dst for each point in src
Input:
    src: Nxm array of points
    dst: Nxm array of points
Output:
    distances: Euclidean distances of the nearest neighbor
    indices: dst indices of the nearest neighbor

Definition at line 48 of file icp.py.