Optimization methodology to fruit grove mapping in precision agriculture

A method capable of efficiently mapping a semi-structured environment is presented.Grove mapping based on LiDAR and the GPS locations of the corner trees is given.An optimization tool that adjusts measurements acquired by a mobile robot is used.The technique was tested in an olive grove located in San Juan - Argentina.It is incorporated a novel filtering technique of unlikely data. The mapping of partially structured agricultural environments is a valuable resource for precision agriculture. In this paper, a technique for the mapping of a fruit grove by a mobile robot is proposed, which uses only front laser information of the environment and the exact position of the grove corners. This method is based on solving an optimization problem with nonlinear constraints, which reduces errors inherent to the measurement process, ensuring an efficient and precise map construction. The resulting algorithm was tested in a real orchard environment. For this, it is also developed a data filtering method capable to comply efficiently the observation-feature matching. The maximum average error obtained by the methodology in simulations was about 13cm, and in real experimentation was about 36cm.