is assigned 1 if the -th point of describes the same point in space as the -th point of . Otherwise is 0. Two things have to be calculated: First, the corresponding points, and second, the transformation (, ) that minimizes on the base of the corresponding points.
The ICP algorithm calculates iteratively the point correspondences. In each iteration step, the algorithm selects the closest points as correspondences and calculates the transformation ( ) for minimizing equation (1). The assumption is that in the last iteration step the point correspondences are correct. Besl et al. prove that the method terminates in a minimum . However, this theorem does not hold in our case, since we use a maximum tolerable distance max for associating the scan data. Such a threshold is required though, given that 3D scans overlap only partially.
In every iteration, the optimal transformation (, ) has to be computed. Eq. (1) can be reduced to
with , since the correspondence matrix can be represented by a vector containing the point pairs.
Four direct methods are known to minimize eq. (2)
. In earlier work
[19,24,25] we used a quaternion based
method , but the following one, based on singular
value decomposition (SVD), is robust and easy to implement, thus
we give a brief overview of the SVD-based algorithm. It was first
published by Arun, Huang and Blostein . The
difficulty of this minimization problem is to enforce the
orthonormality of the matrix . The first step of the
computation is to decouple the calculation of the rotation
from the translation using the centroids of the points
belonging to the matching, i.e.,
The registration calculates the optimal rotation by
. Hereby, the matrices and are derived by the
singular value decomposition
correlation matrix . This
We have proposed and evaluated algorithms to accelerate ICP, namely point reduction and approximate d-trees [19,24,25]. They are used here, too.