An RGB-D multi-view perspective for autonomous agricultural robots

Automated in-field data gathering is essential for crop monitoring and management and for precision farming treatments. To this end, consumer-grade digital cameras have been shown to offer a flexible and affordable sensing solution. This paper describes the integration and development of a cost-effective multi-view RGB-D device for sensing and modeling of agricultural environments. The system features three RGB-D sensors, arranged to cover a horizontal field of view of about 130 deg in front of the vehicle, and a suite of localization sensors consisting of a tracking camera, an RTK-GPS sensor and an IMU device. The system is intended to be mounted on-board an agricultural vehicle to provide multi-channel information of the surveyed scene including color, infrared and depth images, which are then combined with localization data to build a multi-view 3D geo-referenced map of the traversed crop. The experimental demonstrator of the multi-sensor system is presented along with the steps for the integration of the different sensor data into a unique multi-view map. Results of field experiments conducted in a commercial vineyard are included, as well, showing the effectiveness of the proposed system. The resulting map could be useful for precision agriculture applications, including crop health monitoring, and to support autonomous driving.