Self-supervised learning for visual obstacle avoidance: Technical report

Tom van Dijk

doi:10.34641/mg.19

Self-supervised learning for visual obstacle avoidance: Technical report

Tom van Dijk^*

^*Corresponding author for this work

Control & Simulation

Research output: Book/Report › Book › Scientific › peer-review

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Abstract

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.

Original language	English
Publisher	TU Delft, WL/Delft Hydraulics
Number of pages	39
ISBN (Print)	9789463665094
DOIs	https://doi.org/10.34641/mg.19
Publication status	Published - 2022

Keywords

Computer vision
Depth perception
Obstacle avoidance
Self-supervised learning
Stereo vision

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10.34641/mg.19

reportFinal published version, 13.3 MB

Cite this

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KW - Stereo vision

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Self-supervised learning for visual obstacle avoidance: Technical report

Abstract

Keywords

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