Abstract
This study provides an operational solution to directly connect drones to internet by means of 4G telecommunications and exploit drone acquired data, including telemetry and imagery but focusing on video transmission. The novelty of this work is the application of 4G connection to link the drone directly to a data server where video (in this case to monitor road traffic) and imagery (in the case of linear infrastructures) are processed. However, this framework is appliable to any other monitoring purpose where the goal is to send real-time video or imagery to the headquarters where the drone data is processed, analyzed, and exploited. We describe a general framework and analyze some key points, such as the hardware to use, the data stream, and the network coverage, but also the complete resulting implementation of the applied unmanned aerial system (UAS) communication system through a Virtual Private Network (VPN) featuring a long-range telemetry high-capacity video link (up to 15 Mbps, 720 p video at 30 fps with 250 ms of latency). The application results in the real-time exploitation of the video, obtaining key information for traffic managers such as vehicle tracking, vehicle classification, speed estimation, and roundabout in-out matrices. The imagery downloads and storage is also performed thorough internet, although the Structure from Motion postprocessing is not real-time due to photogrammetric workflows. In conclusion, we describe a real-case application of drone connection to internet thorough 4G network, but it can be adapted to other applications. Although 5G will -in time- surpass 4G capacities, the described framework can enhance drone performance and facilitate paths for upgrading the connection of on-board devices to the 5G network.
Highlights
Drones are one of the most fastest growing business of the decade 2010–2020, either for professional or personal uses, and scientific purposes [1,2,3]
The innovation aims at providing high control range Beyond the View Line of Sight of the pilot (BVLOS), with the aircraft flight autonomy. This will allow the integration of deep learning tools, such as Computer Vision (CV) technologies, and big data analysis, adapted to the new autonomous drone fleet paradigm
We provide application details of our own-operated unmanned aerial systems (UAS), while disIn this wcuosrskin, gwiempprloevmideentaaptipolnicatnidoncodmetpaiolns eonftoduertaoiwlsnin-otpheerastyesdteUmAaSn,dwshhialerindgis-the resultant cussing implceommepnlteatteioanrchanitdecctuormalpsocnheenmt ed.eTtahielsininititahleansydsmtemainarnedqusihraerminegntthweasretshueltuannltimited range of communication between the drone and the ground segment, accounting for telemetry and live video to monitor transportation infrastructures and traffic, namely StratoTrans project [18]
Summary
Drones are one of the most fastest growing business of the decade 2010–2020, either for professional or personal uses, and scientific purposes [1,2,3]. We provide application details of our own-operated UAS, while disIn this wcuosrskin, gwiempprloevmideentaaptipolnicatnidoncodmetpaiolns eonftoduertaoiwlsnin-otpheerastyesdteUmAaSn,dwshhialerindgis-the resultant cussing implceommepnlteatteioanrchanitdecctuormalpsocnheenmt ed.eTtahielsininititahleansydsmtemainarnedqusihraerminegntthweasretshueltuannltimited range of communication between the drone and the ground segment (including the pilot GCS and the data processing center), accounting for telemetry and live video to monitor transportation infrastructures and traffic, namely StratoTrans project [18]. This requirement involves overpassing the local radio-link communications between the GCS and the drone Flight Controller (FC). The drone acquired video is sent to a data server to be processed and it is visualized in the operator’s headquarters computer or in a user’s mobile device
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