PHASER: a Robust and Correspondence-free Global Pointcloud Registration

TL;DR

PHASER is a novel, fast, and robust global pointcloud registration method that operates without correspondences, leveraging Fourier analysis to handle noise, sparsity, and partial overlaps effectively.

Contribution

It introduces a correspondence-free registration approach using Fourier analysis, capable of handling multimodal data and estimating uncertainty in rotation and translation.

Findings

Registers pointclouds in under 100ms with 2cm and 0.5deg accuracy

Robust against noise and partial overlaps

Outperforms several existing algorithms on diverse datasets

Abstract

We propose PHASER, a correspondence-free global registration of sensor-centric pointclouds that is robust to noise, sparsity, and partial overlaps. Our method can seamlessly handle multimodal information and does not rely on keypoint nor descriptor preprocessing modules. By exploiting properties of Fourier analysis, PHASER operates directly on the sensor's signal, fusing the spectra of multiple channels and computing the 6-DoF transformation based on correlation. Our registration pipeline starts by finding the most likely rotation followed by computing the most likely translation. Both estimates are distributed according to a probability distribution that takes the underlying manifold into account, i.e., a Bingham and Gaussian distribution, respectively. This further allows our approach to consider the periodic-nature of rotations and naturally represent its uncertainty. We extensively compare PHASER against several well-known registration algorithms on both simulated datasets, and real-world data acquired using different sensor configurations. Our results show that PHASER can globally align pointclouds in less than 100ms with an average accuracy of 2cm and 0.5deg, is resilient against noise, and can handle partial overlap.