apple1987/evdodgenet

EVDodgeNet: Deep Dynamic Obstacle Dodging with event cameras

EVDodgeNet: Deep Dynamic Obstacle Dodging with event cameras

EVDodge by Perception & Robotics Group at the Department of Computer Science, University of Maryland- College Park and Robotics & Perception Group at Department of Informatics, University of Zurich & ETH Zurich.

Code and instruction guidelines will be uploaded here on the acceptance of the paper.

Check out our Youtube video which depicts the proposed framework of our bio-inspired perceptual design for quadrotors.

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 onboard 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 any fine-tuning or retraining.

We successfully evaluate and demonstrate the proposed approach in many real-world experiments with obstacles of different shapes and sizes, achieving an overall success rate of 70% including objects of unknown shape and a low light testing scenario. To our knowledge, this is the first deep learning based solution to the problem of dynamic obstacle avoidance using event cameras on a quadrotor. Finally, we also extend our work to the pursuit task by merely reversing the control policy, proving that our navigation stack can cater to different scenarios.

• Project Page
• Paper
• Supplementary
• arXiv Preprint

Publication:

@inproceedings{Sanket2019EVDodgeEA,
  title={EVDodgeNet: Deep Dynamic Obstacle Dodging with event cameras},
  author={Nitin J. Sanket and Chethan M. Parameshwara and Chahat Deep Singh and Ashwin V. Kuruttukulam and Cornelia Fermuller and Davide Scaramuzza and Yiannis Aloimonos},
  year={2019}
}

License:

Copyright (c) 2019 Perception and Robotics Group (PRG)