This paper introduces a new method of registering point sets. The registration error is directly minimized using general-purpose nonlinear optimization (the Levenberg-Marquardt algorithm). The surprising conclusion of the paper is that this technique is comparable in speed to the special-purpose ICP algorithm which is most commonly used for this task. Because the routine directly minimizes an energy function, it is easy to extend it to incorporate robust estimation via a Huber kernel, yielding a basin of convergence that is many times wider than existing techniques. Finally we introduce a data structure for the minimization based on the chamfer distance transform which yields an algorithm which is both faster and more robust than previously described methods.

This document produced for BMVC 2001