Survey on Computer Vision for UAVs: Current Developments and Trends

By Christoforos Kanellakis, George Nikolakopoulos. Journal of Intelligent and Robotic Systems, 2017

318 citations recorded by the source corpus.

During last decade the scientific research on Unmanned Aerial Vehicless (UAVs) increased spectacularly and led to the design of multiple types of aerial platforms. The major challenge today is the development of autonomously operating aerial agents capable of completing missions independently of human interaction. To this extent, visual sensing techniques have been integrated in the control pipeline of the UAVs in order to enhance their navigation and guidance skills. The aim of this article is to present a comprehensive literature review on vision based applications for UAVs focusing mainly on current developments and trends. These applications are sorted in different categories according to the research topics among various research groups. More specifically vision based position-attitude control, pose estimation and mapping, obstacle detection as well as target tracking are the identified components towards autonomous agents. Aerial platforms could reach greater level of autonomy by integrating all these technologies onboard. Additionally, throughout this article the concept of fusion multiple sensors is highlighted, while an overview on the challenges addressed and future trends in autonomous agent development will be also provided.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/fb39b1a73b37170d22d2588fd5af460c17156ce2

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In Aerospace Engineering

In Computer Vision and Pattern Recognition

Citations in the corpus

Synthetic Data for Deep Learning (2019, background): “Possible applications include SLAM, motion planning, and motion for control for self-driving cars (urban navigation) [177, 346, 411, 435], unmanned aerial vehicles [11, 126, 305], and more; see also general surveys of computer vision for mobile robot navigation [68,142] and perception and control fo”
Survey on Coverage Path Planning with Unmanned Aerial Vehicles (2019, background): “…for low-cost UAVs [15], trajectory planning with and without differential constraints through an environment with obstacles [16], UAV autonomous guidance under uncertainty conditions [17], helicopter navigation and control techniques [11], and perception and state estimation for UAVs [12].”
Comprehensive Review of Artificial Neural Network Applications to Pattern Recognition (2019, background): “ANN has several uses as it provides solutions to simple, complex, and multi-complex challenges [188-191].”
Unmanned aerial vehicles (UAVs): practical aspects, applications, open challenges, security issues, and future trends (2023, background): “ations autonomously in such areas where human intervention is dangerous [7].”