Mobile Robot Localization Based On LowCost LTE And Odometry In GPSDenied Outdoor Environment GPS localization has always been the goto method for localizing mobile robots in outdoor environments. However, in GPS denied environments such as urban canyons, LTE becomes a better alternative. LTE localization exploits existing infrastructures and transmitted signals to provide an estimated position of the robot. As a lowcost solution, it benefits robots under the constraint of cost, size and weight. This paper proposes a particle filter based localization method by using only LTE and wheel odometry for GPSdenied outdoor environments. We used the fingerprinting method by obtaining LTE Cell ID, mean RSS and GPS location and associating these data to the grids in an initialized map. We resolved the position of the robot using recursive Bayesian estimation and particle filter for its implementation. In our experiment, we used a mobile robot and five smartphones to obtain the wheel odometry and LTE data respectively while travelling along a particular route in an outdoor environment. The method was able to obtain accurate localization results with RMSE of 13.07m. We further evaluated the parameters of the method effects on the localization accuracy achieved.

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.