Abstract-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.
The GraphSLAM approach proposed in REF is appropriate for a large-scale environment, since there is no signature uniqueness assumption in the GraphSLAM.
Efficient, generalized indoor WiFi GraphSLAM
2011 IEEE International Conference on Robotics and Automation
2011 IEEE International Conference on Robotics and Automation
Mathematics Computer Science