Dual Quaternion SE(3) Synchronization with Recovery Guarantees
Jianing Zhao, Linglingzhi Zhu, Anthony Man-Cho So
Abstract
Synchronization over the special Euclidean group aims to recover absolute poses from noisy pairwise relative transformations and is a core primitive in robotics and 3D vision. Standard approaches often require multi-step heuristic procedures to recover valid poses, which are difficult to analyze and typically lack theoretical guarantees. This paper adopts a dual quaternion representation and formulates synchronization directly over the unit dual quaternion. A two-stage algorithm is developed: A spectral initializer computed via the power method on a Hermitian dual quaternion measurement matrix, followed by a dual quaternion generalized power method (DQGPM) that enforces feasibility through per-iteration projection. The estimation error bounds are established for spectral estimators, and DQGPM is shown to admits a finite-iteration error bound and achieves linear error contraction up to an explicit noise-dependent threshold. Experiments on synthetic benchmarks and real-world multi-scan point-set registration demonstrate that the proposed pipeline improves both accuracy and efficiency over representative matrix-based methods.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Related papers
- Synchronizing Probability Measures on Rotations via Optimal TransportTolga Birdal, Michael Arbel, Umut Simsekli, Leonidas J. GuibasCVPR 2020
- Pose Synchronization under Multiple Pair-wise Relative PosesYifan Sun, Qixing HuangCVPR 2023
- Efficient and Robust Registration on the 3D Special Euclidean GroupUttaran Bhattacharya, Venu Madhav GovinduICCV 2019 · 21 citations
- Algebraically rigorous quaternion framework for the neural network pose estimation problemChen Lin, Andrew J. Hanson, Sonya M. HansonICCV 2023 · 6 citations
- Optimal least-squares solution to the hand-eye calibration problemAmit Dekel, Linus Härenstam-Nielsen, Sergio CaccamoCVPR 2020
