EVDodge: Embodied AI for High-Speed Dodging on a quadrotor using 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.

In this paper, we develop a purposive artificial intelligence based formulation for the problem of general navigation. We call this AI framework "Embodied AI" - AI design based on the knowledge of agent's hardware limitations and timing/computation constraints. Following this design philosophy we develop a complete AI navigation stack for dodging multiple dynamic obstacles on a quadrotor with a monocular event camera and computation. We also present an approach to directly transfer the shallow neural networks trained in simulation to the real world by subsuming pre-processing using a neural network into the pipeline.

• Project Page
• Paper
• Supplementary
• arXiv Preprint

Publication:

@inproceedings{Sanket2019EVDodgeEA,
  title={EVDodge: Embodied AI For High-Speed Dodging On A Quadrotor Using 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)