TY - GEN
T1 - Event-based Navigation for Autonomous Drone Racing with Sparse Gated Recurrent Network
AU - Andersen, Kristoffer Fogh
AU - Pham, Huy Xuan
AU - Ugurlu, Halil Ibrahim
AU - Kayacan, Erdal
N1 - Publisher Copyright:
© 2022 EUCA.
PY - 2022/8
Y1 - 2022/8
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85135542587
U2 - 10.23919/ECC55457.2022.9838538
DO - 10.23919/ECC55457.2022.9838538
M3 - Article in proceedings
AN - SCOPUS:85135542587
T3 - 2022 European Control Conference, ECC 2022
SP - 1342
EP - 1348
BT - 2022 European Control Conference (ECC)
PB - IEEE
T2 - 2022 European Control Conference, ECC 2022
Y2 - 12 July 2022 through 15 July 2022
ER -