Where am I heading? A robust approach for orientation estimation of autonomous agricultural robots

Reliable knowledge of the vehicle heading plays a significant role in the autonomous navigation of agricultural Unmanned Ground Vehicles (UGVs), especially in the context of unstructured outdoor environments such as rural and forestry scenarios. However, achieving this information with an acceptable degree of confidence is a non-trivial task and still an open field of research. Expensive solutions are available on the market, but they often discourage most farmers due to the large investments needed for the startup. This paper introduces a novel algorithmic solution for reliable evaluation of the absolute vehicle heading, grounded on adaptive Kalman filtering with input evaluation via linear regression analysis. The proposed approach provides a functional and affordable solution to the heading estimation problem that can be used in real-world applications. The system is validated through an extensive experimental campaign using an all-terrain tracked rover operating in agricultural settings, showing good accuracy compared to other approaches, such as a dual GPS method found in the literature.