Self-Supervised Learning for Visual Obstacle Avoidance: Technical report


Tom van Dijk
Delft University of Technology, Faculty of Aerospace Engineering, Micro Air Vehicle Lab
Keywords: computer vision, stereo vision, monocular depth estimation, obstacle avoidance, self-supervised learning, unmanned aerial vehicles, micro aerial vehicles


With a growing number of drones, the risk of collision with other air traffic or fixed obstacles increases. New safety measures are required to keep the operation of Unmanned Aerial Vehicles (UAVs) safe. One of these measures is the use of a Collision Avoidance System (CAS), a system that helps the drone autonomously detect and avoid obstacles.


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cover of self-supervised learning for visual obstacle avoidance report


June 7, 2022


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