Camera Pose Filtering with Local Regression Geodesics on the Riemannian Manifold of Dual Quaternions
Busam B, Birdal T, Navab N (2017)
Publication Type: Conference contribution
Publication year: 2017
Publisher: Institute of Electrical and Electronics Engineers Inc.
Book Volume: 2018-January
Pages Range: 2436-2445
Conference Proceedings Title: Proceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017
Event location: Venice, ITA
ISBN: 9781538610343
Abstract
Time-varying, smooth trajectory estimation is of great interest to the vision community for accurate and well behaving 3D systems. In this paper, we propose a novel principal component local regression filter acting directly on the Riemannian manifold of unit dual quaternions DH1. We use a numerically stable Lie algebra of the dual quaternions together with exp and log operators to locally linearize the 6D pose space. Unlike state of the art path smoothing methods which either operate on SO (3) of rotation matrices or the hypersphere H1 of quaternions, we treat the orientation and translation jointly on the dual quaternion quadric in the 7-dimensional real projective space RP7. We provide an outlier-robust IRLS algorithm for generic pose filtering exploiting this manifold structure. Besides our theoretical analysis, our experiments on synthetic and real data show the practical advantages of the manifold aware filtering on pose tracking and smoothing.
Involved external institutions
Germany (DE)How to cite
APA:
Busam, B., Birdal, T., & Navab, N. (2017). Camera Pose Filtering with Local Regression Geodesics on the Riemannian Manifold of Dual Quaternions. In Proceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017 (pp. 2436-2445). Venice, ITA: Institute of Electrical and Electronics Engineers Inc..
MLA:
Busam, Benjamin, Tolga Birdal, and Nassir Navab. "Camera Pose Filtering with Local Regression Geodesics on the Riemannian Manifold of Dual Quaternions." Proceedings of the 16th IEEE International Conference on Computer Vision Workshops, ICCVW 2017, Venice, ITA Institute of Electrical and Electronics Engineers Inc., 2017. 2436-2445.
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