Self-Supervised Learning for Visual Obstacle Avoidance Technical report

• Päätekijä: van Dijk, Tom (auth)
• Aineistotyyppi: Elektroninen Kirjan osa
• Kieli: englanti
• Julkaistu: Delft TU Delft Open 2022
• Aiheet: Technology, engineering, agriculture; computer vision; stereo vision; monocular depth estimation; obstacle avoidance; self-supervised learning; unmanned aerial vehicles; micro aerial vehicles
• Linkit: DOAB: download the publication; DOAB: description of the publication

Yhteenveto

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.

Ulkoasu

1 electronic resource (48 p.)

ISBN

mg.19
9789463665094
978­94­6366­509­4

Pääsy

Open Access