Self-Supervised Learning for Visual Obstacle Avoidance: Technical report
Keywords:
computer vision, stereo vision, monocular depth estimation, obstacle avoidance, self-supervised learning, unmanned aerial vehicles, micro aerial vehicles
Synopsis
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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Published
June 7, 2022
Categories
Copyright (c) 2022 Tom van Dijk
Details about the available publication format: Download PDF
ISBN-13 (15)
978-94-6366-509-4
Publication date (01)
2022-06-07