Improving agricultural drone localization using georeferenced low-complexity maps

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. In agricultural fields, when operating inside the crops, GPS data are not reliable nor always available, therefore high-precision maps are difficult to be obtained and exploited for in-field operations. Recently, low-complexity, georeferenced 3D maps have been proposed to reduce their computationally demand without losing relevant crop shape information. In this paper, we propose an innovative approach based on the ellipsoid method that allows us to \textit{fuse} the data collected by ultrasonic sensors and the information provided by the simplified map to improve the location estimation of an unmanned ground vehicle within crops. Then, this improved estimation of the vehicle location can be integrated with orientation data, merging it with those provided by other sensors as GPS and IMU, using classical filtering schemes.