Challenges and implemented technologies used in autonomous drone racing

Hyungpil Moon, Jose Martinez-Carranza, Titus Cieslewski, Matthias Faessler, Davide Falanga, Shuo Li, Michaël Ozo, Christophe de Wagter, Guido de Croon, More Authors

Research output: Contribution to journalArticleScientificpeer-review

79 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)137-148
Number of pages12
JournalIntelligent service robotics
Issue number2
Publication statusPublished - 2019


  • Autonomous drone
  • Drone racing
  • Autonomous flight
  • Autonomous navigation


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