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Type Conference or Workshop Paper
Scope Discipline-based scholarship
Published in Proceedings Yes
Title EVDodgeNet: Deep Dynamic Obstacle Dodging with Event Cameras
Organization Unit
Authors
  • Nitin J Sanket
  • Chethan M Parameshwara
  • Chahat Deep Singh
  • Ashwin V Kuruttukulam
  • Cornelia Fermuller
  • Davide Scaramuzza
  • Yiannis Aloimonos
Presentation Type paper
Item Subtype Original Work
Refereed Yes
Status Published in final form
Language
  • English
ISBN 978-1-7281-7395-5
Page Range 10651 - 10657
Event Title 2020 IEEE International Conference on Robotics and Automation (ICRA)
Event Type conference
Event Location Paris, France
Event Start Date July 1 - 2020
Event End Date October 1 - 2020
Publisher IEEE
Abstract Text 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 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.
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Digital Object Identifier 10.1109/icra40945.2020.9196877
Other Identification Number merlin-id:20312
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