PHASER: ロバストで対応点不要な大域的点群位置合わせ
PHASER: a Robust and Correspondence-free Global Pointcloud Registration
フーリエ解析を利用し、対応点や特徴量抽出なしでマルチモーダルな点群を大域的に位置合わせする手法を提案。回転と並進を確率分布として推定し、100ms未満で平均2cm・0.5度の精度を実現した。
著者: Lukas Bernreiter, Lionel Ott, Juan Nieto, Roland Siegwart, Cesar Cadena
分類: cs.RO
原文アブストラクト
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.
