3D Map Reconstruction of an Orchard using an Angle-Aware Covering Control Strategy
This work addresses precision agriculture monitoring by enabling detailed 3D mapping, though it is incremental as it builds on existing covering control methods with specific adaptations.
The paper tackles 3D map reconstruction of an apple orchard using a fleet of drones with an angle-aware covering control strategy, validated through simulations in ROS, achieving effective coverage by integrating multi-spectral data and adaptive updates.
In the last years, unmanned aerial vehicles are becoming a reality in the context of precision agriculture, mainly for monitoring, patrolling and remote sensing tasks, but also for 3D map reconstruction. In this paper, we present an innovative approach where a fleet of unmanned aerial vehicles is exploited to perform remote sensing tasks over an apple orchard for reconstructing a 3D map of the field, formulating the covering control problem to combine the position of a monitoring target and the viewing angle. Moreover, the objective function of the controller is defined by an importance index, which has been computed from a multi-spectral map of the field, obtained by a preliminary flight, using a semantic interpretation scheme based on a convolutional neural network. This objective function is then updated according to the history of the past coverage states, thus allowing the drones to take situation-adaptive actions. The effectiveness of the proposed covering control strategy has been validated through simulations on a Robot Operating System.