  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.
