Abstract
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 language | English |
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Pages (from-to) | 9312-9317 |
Number of pages | 6 |
Journal | IFAC-PapersOnLine |
Volume | 56 |
Issue number | 2 |
DOIs | |
Publication status | Published - 2023 |
Event | 22nd IFAC World Congress - Yokohama, Japan Duration: 9 Jul 2023 → 14 Jul 2023 |
Keywords
- Autonomous robotic systems
- Embedded robotics
- Flying robots
- Monocular depth prediction
- Perception and sensing
- Self-supervised learning