Motion estimation based on point correspondence using neural network

An algorithm for estimating motion parameters of a rigid body from range data is presented. The best correspondence between two three-dimensional point sets is established using a Hopfield neural network. Once the correspondence is built, the δ-bound matching concept is introduced to discard the matched noise pairs and to estimate motion parameters. The method is tolerant to noise and missing points. It is easily extended to plane and surface matching. Simulation results are given for noisy synthetic data