Approach for Supervising Self-localization Processes in Mobile Robots

In this paper it will be presented a proposal of a supervisory approach to be applied to the global localization algorithms in mobile robots. One of the objectives of this work is the increase of the robustness in the estimation of the robot's pose, favoring the anticipated detection of the loss of spatial reference and avoiding faults like tracking derail. The proposed supervisory system is also intended to increase accuracy in localization and is based on two of the most commonly used global feature based localization algorithms for pose tracking in robotics: Augmented Monte Carlo Localization (AMCL) and Perfect Match (PM). The experimental platform was a robotic wheelchair and the navigation used the sensory data from encoders and laser rangers. The software was developed using the ROS framework. The results showed the validity of the proposal, since the supervisor was able to coordinate the action of the AMCL and PM algorithms, benefiting the robot's localization system with the advantages of each one of the methods.