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In this paper, the authors develop a theoretical and experimental framework for the mapping of obstacles (including occluded ones), in a robotic cooperative network, based on a small number of wireless channel measurements. This would allow the robots to map an area before entering it. They consider three approaches based on coordinated space, random space and frequency sampling, and show how the robots can exploit the sparse representation of the map in space, wavelet or spatial variations, in order to build it with minimal sensing. They then show the underlying tradeoffs of all the possible sampling, sparsity and reconstruction techniques. Their simulation and experimental results show the feasibility and performance of the proposed framework.
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