3D Map Reconstruction of an Orchard using an Angle-Aware Covering Control Strategy

TL;DR

This paper introduces a novel UAV-based 3D mapping method for orchards that uses an angle-aware control strategy driven by multispectral data and neural network analysis, enabling adaptive and efficient coverage.

Contribution

It presents an innovative angle-aware covering control strategy for UAVs that integrates multispectral analysis and semantic interpretation for orchard mapping.

Findings

Validated through simulations on ROS.

Effective adaptive coverage achieved.

Improved 3D mapping accuracy.

Abstract

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.