Publication: AlphaPilot: autonomous drone racing
AlphaPilot: autonomous drone racing
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Date
Date
Citations
Foehn, P., Brescianini, D., Kaufmann, E., Cieslewski, T., Gehrig, M., Muglikar, M., & Scaramuzza, D. (2022). AlphaPilot: autonomous drone racing. Autonomous Robots, 46(1), 307–320. https://doi.org/10.1007/s10514-021-10011-y
Abstract
Abstract
Abstract
This paper presents a novel system for autonomous, vision-based drone racing combining learned data abstraction, nonlinear filtering, and time-optimal trajectory planning. The system has successfully been deployed at the first autonomous drone racing world championship: the 2019 AlphaPilot Challenge. Contrary to traditional drone racing systems, which only detect the next gate, our approach makes use of any visible gate and takes advantage of multiple, simultaneous gate detections to compensate for drift in the state estimate and buil
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Creators (Authors)
- Foehn, Philipp
- Brescianini, Dario
- Kaufmann, Elia
- Cieslewski, Titus
- Gehrig, Mathias
- Muglikar, Manasi
- Scaramuzza, Davide
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Citations
Foehn, P., Brescianini, D., Kaufmann, E., Cieslewski, T., Gehrig, M., Muglikar, M., & Scaramuzza, D. (2022). AlphaPilot: autonomous drone racing. Autonomous Robots, 46(1), 307–320. https://doi.org/10.1007/s10514-021-10011-y
