Decentralized and resource-efficient self-calibration of visual sensor networks

摘要

Many multi-camera applications rely on the knowledge of the spatial relationship among the individual nodes. However, establishing such a network-wide calibration is typically a time-consuming task and requires user interaction. In this paper we present a decentralized and resource-aware algorithm for estimating the poses of all network nodes without any user interaction. This self-calibration of the network is achieved in two steps: First, overlapping camera pairs estimate relative positions and orientations by exchanging locally measured distances and angles to detected objects. Second, calibration information of overlapping cameras is spread throughout the network such that poses of non-overlapping cameras can also be estimated. Our approach does not rely on a priori topological information and delivers the extrinsic camera parameters with respect to a common coordinate system. In a simulation study we analyze the performance of our approach concerning the achieved spatial accuracy and computational effort considering noisy measurements and different communication schemes. (C) 2019 Elsevier B.V. All rights reserved.