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International Journal of Computer Vision

Erschienen in:

23.07.2016

MultiCol Bundle Adjustment: A Generic Method for Pose Estimation, Simultaneous Self-Calibration and Reconstruction for Arbitrary Multi-Camera Systems

verfasst von: Steffen Urban, Sven Wursthorn, Jens Leitloff, Stefan Hinz

Erschienen in: International Journal of Computer Vision | Ausgabe 2/2017

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Abstract

In this paper, we present a generic, modular bundle adjustment method for pose estimation, simultaneous self-calibration and reconstruction for multi-camera systems. In contrast to other approaches that use bearing vectors (camera rays) as observations, we extend the common collinearity equations with a general camera model and include the relative orientation of each camera w.r.t to the fixed multi-camera system frame yielding the extended collinearity equations that directly express all image observations as functions of all unknowns. Hence, we can either calibrate the camera system, the cameras, reconstruct the observed scene, and/or simply estimate the pose of the system by including the corresponding parameter block into the Jacobian matrix. Apart from evaluating the implementation with comprehensive simulations, we benchmark our method against recently published methods for pose estimation and bundle adjustment for multi-camera systems. Finally, all methods are evaluated using a 6 degree of freedom ground truth data set, that was recorded with a lasertracker.

Vorheriger Artikel Minimal Basis Subspace Representation: A Unified Framework for Rigid and Non-rigid Motion Segmentation

Nächster Artikel Large-Scale Gaussian Process Inference with Generalized Histogram Intersection Kernels for Visual Recognition Tasks

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Titel: MultiCol Bundle Adjustment: A Generic Method for Pose Estimation, Simultaneous Self-Calibration and Reconstruction for Arbitrary Multi-Camera Systems
verfasst von: Steffen Urban
Sven Wursthorn
Jens Leitloff
Stefan Hinz
Publikationsdatum: 23.07.2016
Verlag: Springer US
Erschienen in: International Journal of Computer Vision / Ausgabe 2/2017
Print ISSN: 0920-5691
Elektronische ISSN: 1573-1405
DOI: https://doi.org/10.1007/s11263-016-0935-0

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