Hybrid Localization Solutions Enabling Autonomous Navigation of Robotic Platforms

Recently, autonomous navigation of mobile robotic platforms has received increasing attention from the research community. Both outdoor and indoor localization is very crucial as it enables the navigation of Autonomous Mobile Robots. Autonomous Mobile Robots can be employed for a wide range of applications including logistic operations, automated inventory management in warehouses, inspection, and monitoring tasks in industrial plants, precision agriculture, and critical infrastructure monitoring without the need for humans. This research aims not only to provide positioning solutions for autonomous navigation but also to improve and optimize their accuracy and reliability for autonomous navigation. The typical solution for indoor positioning is Ultra-Wide Band (UWB), and for outdoor positioning is Global Navigation Satellite Systems (GNSS); these positioning solutions was integrated with IMU (also called Hybrid solution) using the help of ESKF (Error State Kalman Filter) for attitude estimation and a more accurate positioning estimation. The goal of this thesis is to design a hybrid localization algorithm that combines both UWB/GNSS and IMU data, enabling autonomous operations of robotic platform in desired environments. The designed algorithm was tested using both simulated data and real measurement data and optimized iteratively. The indoor localization tests were conducted in the Robotic Laboratory provided by LINKS Foundation, while the outdoor localization tests took place on the sidewalks within the Politecnico di Torino area. These tests demonstrated the algorithm’s capability to support autonomous navigation in dynamic environments.