Mobile Robot Localization with Reinforcement Learning Map Update Decision aided by an Absolute Indoor Positioning System This paper introduces a new mobile robot localization solution consisting of two main modules: a ParticleFilter based Localization (PFL) and a ReinforcementLearning based map updating, integrating relative measurements and absolute indoor positioning sensor (AIPS) data. Concerning localization using 2DLiDARs, featureless areas are known to be problematic. To solve this problem a classic PFL approach was modified to incorporate AIPS position measurements in the prediction and update stages. The localization approach has the particularity of including the possibility of updating the map whenever major modifications are detected in the environment in relation to the current localization map. Due to the random samplingbased nature of the PFL, an associated map update solution is not trivial since small inconsistencies in the estimated pose can lead to erroneous map associations. The proposed method learns to decide by assigning higher rewards the greater is the overlap between the map and the 2DLIDAR scans, via RL, and then a proper update of the map is achieved. Validation of the proposed pipeline was carried out in a differential drive platform with algorithms developed in ROS. Tests were performed in two scenarios in order to assess the performance of both the localization module and the map update stage. The results show that the proposed localization method offers improvements in relation to known approaches, and consequently suggest promising perspectives for the proposed map update decision framework.

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.