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

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• 作者：Steffen Urban ; Sven Wursthorn ; Jens Leitloff…
• 关键词：Camera ; Calibration ; Accuracy ; Precision ; Geometric ; Robotics ; Bundle
• 刊名：International Journal of Computer Vision
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：121
• 期：2
• 页码：234-252
• 全文大小：
• 刊物类别：Computer Science
• 刊物主题：Computer Imaging, Vision, Pattern Recognition and Graphics; Artificial Intelligence (incl. Robotics); Image Processing and Computer Vision; Pattern Recognition;
• 出版者：Springer US
• ISSN：1573-1405
• 卷排序：121

文摘

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.