The adoption of autonomous vehicles in agricultural scenarios can aspire to become a reality if the validation of their effectiveness is sustained by the contemporary and shared improvement of all those technological gaps identified by current research projects. In particular, a crucial point is still linked to the autonomy of robots, related to the concept of guidance, navigation and control. Using a combination of conceptually different sensing techniques and integrating the subsequent data, more accurate property estimates can be provided, leading to more robust management and increased adoptability of sensor-based crop management. For example, to properly locate and operate autonomous vehicles for in-field tasks, the knowledge of their instantaneous position needs to be combined with an accurate spatial description of their environment.