Efficient, generalized indoor WiFi GraphSLAM

Efficient, generalized indoor WiFi GraphSLAM,10.1109/ICRA.2011.5979643,Joseph Huang,David Millman,Morgan Quigley,David Stavens,Sebastian Thrun,Alok Ag

Efficient, generalized indoor WiFi GraphSLAM  
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The widespread deployment of wireless networks presents an opportunity for localization and mapping using only signal-strength measurements. The current state of the art is to use Gaussian process latent variable models (GP-LVM). This method works well, but relies on a signature uniqueness assumption which limits its applicability to only signal-rich environments. Moreover, it does not scale computationally to large sets of data, requiring O N 3 operations per iteration. We present a GraphSLAM-like algorithm for signal strength SLAM. Our algorithm shares many of the benefits of Gaussian processes, yet is viable for a broader range of environments since it makes no signature uniqueness assumptions. It is also more tractable to larger map sizes, requiring O N 2 operations per iteration. We compare our algorithm to a laser-SLAM ground truth, showing it produces excellent results in practice.
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