Publication: EVDodgeNet: Deep Dynamic Obstacle Dodging with Event Cameras
EVDodgeNet: Deep Dynamic Obstacle Dodging with Event Cameras
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Sanket, N. J., Parameshwara, C. M., Singh, C. D., Kuruttukulam, A. V., Fermuller, C., Scaramuzza, D., & Aloimonos, Y. (2020). EVDodgeNet: Deep Dynamic Obstacle Dodging with Event Cameras. 10651–10657. https://doi.org/10.1109/icra40945.2020.9196877
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Dynamic obstacle avoidance on quadrotors requires low latency. A class of sensors that are particularly suitable for such scenarios are event cameras. In this paper, we present a deep learning based solution for dodging multiple dynamic obstacles on a quadrotor with a single event camera and on-board computation. Our approach uses a series of shallow neural networks for estimating both the ego-motion and the motion of independently moving objects. The networks are trained in simulation and directly transfer to the real world without a
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Sanket, N. J., Parameshwara, C. M., Singh, C. D., Kuruttukulam, A. V., Fermuller, C., Scaramuzza, D., & Aloimonos, Y. (2020). EVDodgeNet: Deep Dynamic Obstacle Dodging with Event Cameras. 10651–10657. https://doi.org/10.1109/icra40945.2020.9196877