Lab Meeting Dec. 22, 2011 (Wang Li): Fast Point Feature Histograms (FPFH) for 3D Registration (ICRA 2009)

Fast Point Feature Histograms (FPFH) for 3D Registration

Radu Bogdan Rusu
Nico Blodow
Michael Beetz

Abstract

In this paper, we modify the mathematical expressions of Point Feature Histograms (PFH), and perform a rigorous analysis on their robustness and complexity for the problem of 3D registration. More concretely, we present optimizations that reduce the computation times drastically by either caching previously computed values or by revising their theoretical formulations. The latter results in a new type of local features, called Fast Point Feature Histograms (FPFH), which retain most of the discriminative power of the PFH. Moreover, we propose an algorithm for the online computation of FPFH features, demonstrate their efficiency for 3D registration, and propose a new sample consensus based method for bringing two datasets into the convergence basin of a local non-linear optimizer: SAC-IA (SAmple Consensus Initial Alignment).

Paper Link