Physics

Point cloud

Metric (unit)

Iterative closest point

Degrees of freedom (physics and chemistry)

Mathematics

Economic growth

Politics

Algorithm

Image (mathematics)

Square (algebra)

Political science

Artificial intelligence

Economics

Image registration

Set (abstract data type)

Programming language

Operations management

Geometry

Representation (politics)

Convergence (economics)

Computer science

Quantum mechanics

Point (geometry)

IEEE Transactions on Pattern Analysis and Machine Intelligence

The authors describe a general-purpose, representation-independent method for the accurate and computationally efficient registration of 3-D shapes including free-form curves and surfaces. The method handles the full six degrees of freedom and is based on the iterative closest point (ICP) algorithm, which requires only a procedure to find the closest point on a geometric entity to a given point. The ICP algorithm always converges monotonically to the nearest local minimum of a mean-square distance metric, and the rate of convergence is rapid during the first few iterations. Therefore, given an adequate set of initial rotations and translations for a particular class of objects with a certain level of 'shape complexity', one can globally minimize the mean-square distance metric over all six degrees of freedom by testing each initial registration. One important application of this method is to register sensed data from unfixtured rigid objects with an ideal geometric model, prior to shape inspection. Experimental results show the capabilities of the registration algorithm on point sets, curves, and surfaces.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

A method for registration of 3-D shapes