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Range-Only Robot Localization and SLAM with Radio

This paper presents three localization algorithms using radio beacons that provide the ability to measure range only. Obtaining range from radio has the advantage that line of sight between the beacons and the transponder is not required, and the data association problem can be completely avoided. An extended Kalman filter, a particle filter, and a nonlinear sliding batch method are used to fuse range data with dead reckoning data collected from a real system, and results are demonstrated. The extended Kalman filter is also applied to the SLAM problem.