Wireless signal strength based localization
Most up-to-date implementations can be at my github: Main code in python, ROS package
Datasets are also available here
WGPPL: Wireless signal strength modeling using Gaussian Processes and Path loss models
WiFi-based self-localization for indoor robots
Related publications:
Renato Miyagusuku, Atsushi Yamashita and Hajime Asama: “Data Information Fusion from Multiple Access Points for WiFi-based Self-localization”, IEEE Robotics and Automation Letters, Vol. 4, No. 2, pp. 269-276, April 2019. [doi:10.1109/LRA.2018.2885583]
Renato Miyagusuku, Atsushi Yamashita and Hajime Asama:”Precise and accurate wireless signal strength mappings using Gaussian processes and path loss models”, Robotics and Autonomous Systems, February 2018. [doi:10.1016/j.robot.2018.02.011]
Renato Miyagusuku, Atsushi Yamashita and Hajime Asama: “Gaussian Processes Mappings Improvements Using Path Loss Models for Wireless Signals-based Localization”, in: IEEE/RSJ International Conference on Intelligent Robots and Systems, 2016, pp. 4610-4615. [doi:10.1109/IROS.2016.7759678]
* This work employs a previous python implementation available here
WLRF: WiFi and range data fusion
Main implementations for fusing WiFi and range data: Modified amcl, wlrf ROS package
How to generate additional rosbags for testing global localization and the kidnapped robot problem can be found here
Related publications: