Paper Info
Title
Challenges and implemented technologies used in autonomous drone racing
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.
Year
DOI
Venue
2019
10.1007/s11370-018-00271-6
Intelligent Service Robotics
Keywords
Field
DocType
Autonomous drone, Drone racing, Autonomous flight, Autonomous navigation
Computer vision,Motion control,Software deployment,Computer science,Sensor fusion,Real-time computing,Waypoint,Artificial intelligence,Drone,Deep learning,Motion estimation,Benchmark (computing)
Journal
Volume
Issue
ISSN
12
2
1861-2784
Citations 
PageRank 
References 
4
0.46
7
Authors
18
Name
Order
Citations
PageRank
Hyungpil Moon117538.32
José Martínez-Carranza2204.10
Titus Cieslewski3264.65
Matthias Faessler41458.66
Davide Falanga540.46
Alessandro Simovic640.46
Davide Scaramuzza72704154.51
Shuo Li86113.39
Michael Ozo940.46
Christophe De Wagter10265.42
guido c h e de croon11399.35
Sunyou Hwang1240.46
Sunggoo Jung13132.14
Hyunchul Shim1440.46
Haeryang Kim1540.46
Minhyuk Park1640.46
Tsz-Chiu Au1740.46
Si Jung Kim1840.46