受约束的自校准

J. Mendelsohn, Kostas Daniilidis
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引用次数: 11

摘要

本文主要研究了从图像序列中估计相机的固有参数和相机的运动轨迹。内置相机校准和姿态估计是涉及导航任务,场景重建以及虚拟和真实环境合并的许多应用程序的先决条件。提出并评估了一种通过在环境中放置易于识别的已知形状的目标来降低自校准灵敏度的技术解决方案。不需要先验地知道目标的相对位置。假设大小与距离的适当比例,这些目标解决了已知的模糊性。探讨了目标位置和摄像机运动的约束条件。该算法在各种现实世界的场景中进行了广泛的测试。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Constrained self-calibration
This paper focuses on the estimation of the intrinsic camera parameters and the trajectory of the camera from an image sequence. Intrinsic camera calibration and pose estimation are the prerequisites for many applications involving navigation tasks, scene reconstruction, and merging of virtual and real environments. Proposed and evaluated is a technical solution to decrease the sensitivity of self-calibration by placing easily identifiable targets of known shape in the environment. The relative position of the targets need not be known a priori. Assuming an appropriate ratio of size to distance these targets resolve known ambiguities. Constraints on the target placement and the cameras' motions are explored. The algorithm is extensively tested in a variety of real-world scenarios.
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