Nano Quadcopter Obstacle Avoidance with a Lightweight Monocular Depth Network

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In this paper, we propose an obstacle avoidance solution for a 34-gram quadcopter equipped with a monocular camera. The perception of obstacles is tackled by a lightweight convolutional neural network predicting a dense depth map from a captured grey-scale image. The depth network performs self-supervised learning and thus requires no ground-truth labels that are costly to acquire. Based on the depth map, the control strategy is implemented by a behavior state machine that balances the efficiency to explore the environment and the safety of avoiding obstacles. In real-world flight experiments, our solution demonstrates the efficacy of predicting trust-worthy depth maps and a stable control strategy in various cluttered environments.

Original languageEnglish
Pages (from-to)9312-9317
Number of pages6
Issue number2
Publication statusPublished - 2023
Event22nd IFAC World Congress - Yokohama, Japan
Duration: 9 Jul 202314 Jul 2023


  • Autonomous robotic systems
  • Embedded robotics
  • Flying robots
  • Monocular depth prediction
  • Perception and sensing
  • Self-supervised learning


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