Seamless Indoor-Outdoor Autonomous Navigation for Unmanned Ground Vehicles

Precision Agriculture (PA) concept has been gaining popularity in the last few years, and it is seen as a possible solution to meet the needs of the growing world population. New techniques and approaches are being developed; these innovations are meant to increase productivity and maximize profit while being a foundation of sustainable agriculture. Autonomous robots have a crucial role in this application. In particular, this work is intended to investigate the problem of Autonomous Navigation to provide a solution of seamless Indoor and Outdoor Navigation. An example of employment could be the forage distribution in large farm stables, where the recharge point is usually outside the building. The Navigation problem and solution for the two environments differ. For the Outdoor navigation, a combination of Global Navigation Satellite System (GNSS) and Inertial Measurement Unit supplies enough data for estimating the position and orientation. In Indoor scenarios, the vehicle needs a static map and a range sensor (LiDaR) data to localize itself. Robot Operating System's (ROS) tools, as services and messages, have been used as a framework to develop and run the application due to its modularity and simple interfaces. In order to obtain good performances, the sensor data are processed by two different filters: a Particle Filter and an Extended Kalman Filter. The first is the foundation of the Adaptive Monte Carlo Localization (AMCL) utilized in indoor environments, while the second is for outdoor application. Then, to navigate seamlessly, a switching algorithm selects which pose estimate to use from the two filters. The feasibility and accuracy of this approach have been tested through several simulations and then deployed on a real rover. The results of these experiments are illustrated in the last chapters of this work.