Efficient, Generalized Indoor WiFi GraphSLAM
The widespread deployment of wireless networks presents an opportunity for localization and mapping using only signal-strength measurements. The current state of the art 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(N3) 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(N2)operations per iteration. We compare our algorithm to a laser-SLAM ground truth, showing it produces excellent results in practice.
Joseph Huang David Millman Morgan Quigley David Stavens Sebastian Thrun Alok Aggarwal
国际会议
2011 IEEE International Conference on Robotics and Automation(2011年IEEE世界机器人与自动化大会 ICRA 2011)
上海
英文
1038-1043
2011-05-09（万方平台首次上网日期，不代表论文的发表时间）