Sensor Localization and Camera Calibration in Distributed Camera Sensor Networks

Andrew Barton-Sweeney, Dimitrios Lymberopoulos, A. Savvides
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引用次数: 75

Abstract

Camera sensors constitute an information rich sensing modality with many potential applications in sensor networks. Their effectiveness in a sensor network setting however greatly relies on their ability to calibrate with respect to each other, and other sensors in the field. This paper examines node localization and camera calibration using the shared field of view of camera pairs. Using a new distributed camera sensor network we compare two approaches from computer vision and propose an algorithm that combines a sparse set of distance measurements with image information to accurately localize nodes in 3D. Our algorithms are evaluated using a network of iMote2 nodes equipped with COTS camera modules. The sensor nodes identify themselves to cameras using modulated LED emissions. Our indoor experiments yielded a 2-7cm error in a 6x6m room. Our outdoor experiments in a 30x30m field resulted in errors 20-80cm, depending on the method used.
分布式摄像机传感器网络中的传感器定位与摄像机标定
相机传感器是一种信息丰富的传感方式,在传感器网络中具有广泛的应用前景。然而,它们在传感器网络设置中的有效性在很大程度上依赖于它们相对于彼此和现场其他传感器的校准能力。本文研究了利用摄像机对共享视场的节点定位和摄像机标定问题。利用一种新的分布式相机传感器网络,我们比较了计算机视觉的两种方法,并提出了一种将稀疏的距离测量集与图像信息相结合的算法,以准确地定位3D中的节点。我们的算法使用配备COTS相机模块的iMote2节点网络进行评估。传感器节点通过调制LED辐射向摄像机识别自己。我们的室内实验在一个6 × 6米的房间里产生了2-7厘米的误差。我们在30x30m的野外进行室外实验,误差为20-80cm,具体取决于使用的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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