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PHASER: A Robust and Correspondence-Free Global Pointcloud Registration

Publication date:

  • 18 January 2021

Authors:

Lukas Bernreiter
Lionel Ott
Juan Nieto
Roland Siegwart
Cesar Cadena

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.

Reference
Doi: 10.1109/LRA.2021.3052418
Published in: IEEE Robotics and Automation Letters ,
 Volume 6,
 Issue 2,
 Pages 855 – 862
Read the paper
Version: Published
Access: Open
Download
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