Event-based Navigation for Autonomous Drone Racing with Sparse Gated Recurrent Network

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1 Citation (Scopus)


Event-based vision has already revolutionized the perception task for robots by promising faster response, lower energy consumption, and lower bandwidth without introducing motion blur. In this work, a novel deep learning method based on gated recurrent units utilizing sparse convolutions for detecting gates in a race track is proposed using event-based vision for the autonomous drone racing problem. We demonstrate the efficiency and efficacy of the perception pipeline on a real robot platform that can safely navigate a typical autonomous drone racing track in real-time. Throughout the experiments, we show that the event-based vision with the proposed gated recurrent unit and pretrained models on simulated event data significantly improve the gate detection precision. Furthermore, an event-based drone racing dataset11The code and data will be available at https://github.com/open-airlab/neuromorphic-au-drone-racing.git consisting of both simulated and real data sequences is publicly released.

Original languageEnglish
Title of host publication2022 European Control Conference (ECC)
Number of pages7
Publication dateAug 2022
ISBN (Electronic)9783907144077
Publication statusPublished - Aug 2022
Event2022 European Control Conference, ECC 2022 - London, United Kingdom
Duration: 12 Jul 202215 Jul 2022


Conference2022 European Control Conference, ECC 2022
Country/TerritoryUnited Kingdom
Series2022 European Control Conference, ECC 2022


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