Unmanned Aerial Vehicle’s Obstacle Avoidance Research Based on Vision
- DOI
- 10.2991/978-94-6463-300-9_51How to use a DOI?
- Keywords
- unmanned aerial vehicle; Obstacle Avoidance; Obstacle Detection; Vision
- Abstract
With the increasingly widespread application scenarios of drones, higher requirements have been put forward for the autonomous flight capability of drones. The autonomous obstacle avoidance technology of unmanned aerial vehicles plays an important role in various environments that are inconvenient for pilots to operate. With the rise of image recognition technology, visual sensors have become the mainstream choice for obstacle avoidance in unmanned aerial vehicles. There are currently many autonomous obstacle avoidance systems based on visual design, and the chosen solutions are too broad. This article aims to introduce the current development status of visual obstacle avoidance, categorizing obstacle avoidance schemes based on the number of cameras used, and helping researchers choose appropriate obstacle avoidance schemes. This article provides a certain degree of analysis of obstacle recognition and avoidance solutions for drone obstacle avoidance, as well as the problems faced in development and possible future solutions. Visual obstacle avoidance technology still needs to overcome many obstacles.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Wenzhe Wang PY - 2023 DA - 2023/11/27 TI - Unmanned Aerial Vehicle’s Obstacle Avoidance Research Based on Vision BT - Proceedings of the 2023 International Conference on Image, Algorithms and Artificial Intelligence (ICIAAI 2023) PB - Atlantis Press SP - 501 EP - 508 SN - 2352-538X UR - https://doi.org/10.2991/978-94-6463-300-9_51 DO - 10.2991/978-94-6463-300-9_51 ID - Wang2023 ER -