Nano Quadcopter Obstacle Avoidance with a Lightweight Monocular Depth Network
C. Liu (TU Delft - Control & Simulation)
Yingfu Xu (TU Delft - Control & Simulation)
EJ van Kampen (TU Delft - Control & Simulation)
G. C. H. E. de Croon (TU Delft - Control & Simulation)
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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.
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