Point cloud registration: matching a maximal common subset on pointclouds with noise (with 2D implementation)
This addresses fingerprint matching with incomplete data, but the method appears incremental as it adapts an existing electrostatic-inspired approach to a specific registration task.
The paper tackles the problem of finding maximal common subsets in 2D point clouds with noise and outliers, proposing an algorithm that optimizes a potential energy function to achieve this.
We analyze the problem of determining whether 2 given point clouds in 2D, with any distinct cardinality and any number of outliers, have subsets of the same size that can be matched via a rigid motion. This problem is important, for example, in the application of fingerprint matching with incomplete data. We propose an algorithm that, under assumptions on the noise tolerance, allows to find corresponding subclouds of the maximum possible size. Our procedure optimizes a potential energy function to do so, which was first inspired in the potential energy interaction that occurs between point charges in electrostatics.