Point set registration via stochastic particle flow filter

Abstract

I present an innovative registration algorithm using the particle flow filter. The particle flow filter is a Bayesian filter that uses particles to represent probability densities. The particle flow filter is not constrained to the highly restrictive unimodal, linear, and Gaussian assumptions of many Bayesian filters such as the Kalman filter. Additionally, the particle flow filter is computationally more efficient than other multimodal filters such as the better-known particle filter (PF). Unlike the PF, the particle flow filter does not require particle resampling or importance weight updates. Rather, the proposal density is formed by flowing the prior probability to the posterior using the Fokker–Planck equation. The particle flow filter algorithms were implemented using MATLAB. Both 2D and 3D rigid body point-set registration were conducted using the Gromov particle flow filter variant. Additionally, the PF method and iterative closest point (ICP) algorithms were implemented for comparison. For the same alignment accuracy, the new particle flow filter approach was 244% faster than the PF for certain challenging problems. For the same alignment time, the particle flow filter reduced misalignment by as much as 35% over that of the PF. The particle flow filter achieved 100% alignment with enough particles, and reduced misalignment by as much as 75% over that of ICP. These results demonstrate that image registration via the particle flow filter significantly outperforms the PF and ICP algorithms in the presence of noise and for a high degree of initial misalignment.