Further Results with Localization and Mapping Using Range from Radio. In this paper, we present recent results with using range from radio for mobile robot localization. In previous work we have shown how range readings from radio tags placed in the environment can be used to localize a robot. We have extended previous work to consider robustness. Specifically, we are interested in the case where range readings are very noisy and available intermittently. Also, we consider the case where the location of the radio tags is not known at all ahead of time and must be solved for simultaneously along with the position of the moving robot. We present results from a mobile robot that is equipped with GPS for ground truth, operating over several km.

Outdoor Localization System with Augmented State Extended Kalman Filter and RadioFrequency Received Signal Strength In this paper, we propose a mobile robot localization system based on the Received Signal Strength Indicator (RSSI) of three radio module beacons, to enhance or replace GPS positioning in scenarios with little or no GPS availability, such as urban environments with blocked sky view (near buildings or tall trees). Our approach uses an Extended Kalman Filter with augmented states to estimate a robotu0027s trajectory by fusing sensor data from odometry, gyroscope, GPS (when available), and three RSSI from the beacons, which have its biases corrected in the Kalman filter itself. Our contribution is a lowcost localization system to be used in outdoor environments where other sensors suffer from a lack of availability and uncertainty. To test our approach, we experiment by collecting several datasets in an outdoor environment with a wheeled ground robot, where it travels in a predefined trajectory with a total length of 48.78 meters. We use these datasets to estimate the robotu0027s trajectory offline, where our approach achieves a best mean trajectory error of 0.308 meters, an 82.58% improvement over the baseline GPS error.