Self-Supervised Learning for Visual Obstacle Avoidance: Technical report

Authors

Tom van Dijk
Delft University of Technology, Faculty of Aerospace Engineering, Micro Air Vehicle Lab
https://orcid.org/0000-0002-0772-3821
DOI: https://doi.org/10.34641/mg.19
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

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Copyright (c) 2022 Tom van Dijk

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Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

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ISBN-13 (15)

978-94-6366-509-4

Publication date (01)

2022-06-07