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.
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