A recursive robust filtering approach for 3D registration

TL;DR

This paper introduces a recursive robust filtering method for 3D registration that effectively handles noise, uncertainties, and local minima, resulting in accurate and stable alignment of 3D data.

Contribution

It proposes a novel recursive filtering approach combining multiple norms for improved robustness and convergence in feature-based 3D registration.

Findings

Effective noise handling in sensory data

Robustness to feature localization uncertainties

Validated on physical and synthetic datasets

Abstract

This work presents a new recursive robust filtering approach for feature-based 3D registration. Unlike the common state-of-the-art alignment algorithms, the proposed method has four advantages that have not yet occurred altogether in any previous solution. For instance, it is able to deal with inherent noise contaminating sensory data; it is robust to uncertainties caused by noisy feature localisation; it also combines the advantages of both (Formula presented.) and (Formula presented.) norms for a higher performance and a more prospective prevention of local minima. The result is an accurate and stable rigid body transformation. The latter enables a thorough control over the convergence regarding the alignment as well as a correct assessment of the quality of registration. The mathematical rationale behind the proposed approach is explained, and the results are validated on physical and synthetic data.