Camera networks are perhaps the most common type of sensor network and are deployed in a variety of real-world applications including surveillance, intelligent environments and scientific remote monitoring. A key problem in deploying a network of cameras is calibration, i.e., determining the location and orientation of each sensor so that observations in an image can be mapped to locations in the real world. This paper proposes a fully distributed approach for camera network calibration. The cameras collaborate to track an object that moves through the environment and reason probabilistically about which camera poses are consistent with the observed images. This reasoning employs sophisticated techniques for handling the difficult nonlinearities imposed by projective transformations, as well as the dense correlations that arise between distant cameras. Our method requires minimal overlap of the cameras' fields of view and makes very few assumptions about the motion of the object. In contrast to existing approaches, which are centralized, our distributed algorithm scales easily to very large camera networks. We evaluate the system on a real camera network with 25 nodes as well as simulated camera networks of up to 50 cameras and demonstrate that our approach performs well even when communication is lossy.

Abstract-Internal localization, the problem of estimating relative pose for each module (part) of a modular robot is a prerequisite for many shape control, locomotion, and actuation algorithms. In this paper, we propose a robust hierarchical approach that uses normalized cut to identify dense subregions with small mutual localization error, then progressively merges those subregions to localize the entire ensemble. Our method works well in both 2D and 3D, and requires neither exact measurements nor rigid inter-module connectors. Most of the computations in our method can be effectively distributed. The result is a robust algorithm that scales to large, non-homogeneous ensembles. We evaluate our algorithm in accurate 2D and 3D simulations of scenarios with up to 10,000 modules.

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In this article we describe a concept for a new type of material, which we call claytronics , made out of very large numbers-potentially millionsof submillimeter-sized spherical robots. While still only a concept, we have completed a considerable amount of initial design and experimentation work, enough at this point to allow us to understand what is readily achievable within a short time frame (less than a decade) and also to identify some of the most significant technical challenges yet to be overcome. To date, we have developed and analyzed several promising engineering designs, conducted numerous large-scale experiments on a high-fidelity physics-based simulator, and successfully carried out several prototype three-dimensional (3D) microelectromechanical systems (MEMS) manufacturing runs. These experiences lead us to believe that there are no fundamental software or hardware barriers to realizing claytronics on a large scale and within a few years.While the most fundamental purpose of our research on claytronics is to understand manufacturing and programming of very large ensembles of independently actuated computing devices, it is also clear that such a material would have numerous practical applications, ranging from shape-shifting radio antennas (important for software-defined radios) to 3D fax machines. Perhaps our most fanciful-sounding application, however, is motivated by one of the most basic of human needs: to communicate and interact with others. Two centuries ago, the only practical way to carry on a real-time conversation with