Abstract
Autonomous drone racing (ADR) is a challenge for autonomous drones to navigate a cluttered indoor environment without relying on any external sensing in which all the sensing and computing must be done with onboard resources. Although no team could complete the whole racing track so far, most successful teams implemented waypoint tracking methods and robust visual recognition of the gates of distinct colors because the complete environmental information was given to participants before the events. In this paper, we introduce the purpose of ADR as a benchmark testing ground for autonomous drone technologies and analyze challenges and technologies used in the two previous ADRs held in IROS 2016 and IROS 2017. Five teams which participated in these events present their implemented technologies that cover modified ORB-SLAM, robust alignment method for waypoints deployment, sensor fusion for motion estimation, deep learning for gate detection and motion control, and stereo-vision for gate detection.
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Acknowledgements
J. Martinez-Carranza is thankful for the funding received by the Royal Society through the Newton Advanced Fellowship with reference NA140454. Team UZH thanks Elia Kaufmann, Antoni Rosinol Vidal, and Henri Rebecq for their great help in the software implementation and integration. Team of TU Delft would like to thank the organizers of the Autonomous Drone Race event. Team UNIST’s work was supported by NRF (2.180186.01 and 2.170511.01). All authors would like to thank the organizers of the Autonomous Drone Racing.
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Moon, H., Martinez-Carranza, J., Cieslewski, T. et al. Challenges and implemented technologies used in autonomous drone racing. Intel Serv Robotics 12, 137–148 (2019). https://doi.org/10.1007/s11370-018-00271-6
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DOI: https://doi.org/10.1007/s11370-018-00271-6