A survey on vision-based UAV navigation

By Yuncheng Lu, Zhucun Xue, Gui-Song Xia, Liangpei Zhang. Geo-Spatial Information Science, 2018

420 citations recorded by the source corpus.

Abstract Research on unmanned aerial vehicles (UAV) has been increasingly popular in the past decades, and UAVs have been widely used in industrial inspection, remote sensing for mapping & surveying, rescuing, and so on. Nevertheless, the limited autonomous navigation capability severely hampers the application of UAVs in complex environments, such as GPS-denied areas. Previously, researchers mainly focused on the use of laser or radar sensors for UAV navigation. With the rapid development of computer vision, vision-based methods, which utilize cheaper and more flexible visual sensors, have shown great advantages in the field of UAV navigation. The purpose of this article is to present a comprehensive literature review of the vision-based methods for UAV navigation. Specifically on visual localization and mapping, obstacle avoidance and path planning, which compose the essential parts of visual navigation. Furthermore, throughout this article, we will have an insight into the prospect of the UAV navigation and the challenges to be faced.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/ea00bbb6908f615a132e3b6d7ba9908acd92b280

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Jorge Peña Queralta
2. Madhusanka Liyanage
3. Tomi Westerlund
4. Shen Wang
5. Patrick McEnroe

In Computer Vision and Pattern Recognition

1. Jorge Peña Queralta
2. Madhusanka Liyanage
3. Hannu Tenhunen
4. Tomi Westerlund
5. Bilge Can Pullinen
6. Shen Wang
7. Jenni Raitoharju
8. Tuan Nguyen Gia

Citations in the corpus

• A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges (2022, background): “There are many different types of path planning techniques, however they all try to find an optimal (or near optimal) path based on performance indicators, such as shortest time, shortest route, or minimum cost of work [68].”
• Collaborative Multi-Robot Search and Rescue: Planning, Coordination, Perception, and Active Vision (2020, background): “Among the different approaches to onboard odometry, visual methods have gained significant traction due to their low price, passive nature and flexibility [112].”