Abstract
In recent years, there has been significant growth in applications of Unmanned Aerial Vehicles (UAVs). The demand for an autonomous UAV navigation is growing due to various applications in GPS-denied environments like disaster relief monitoring, search and rescue, mining, bridge inspections, space explorations, and military activities. Visual measurements possess a lot of accurate information which is extracted and exploited for UAV manoeuvring. This paper presents a comprehensive survey of vision-based UAV manoeuvring techniques. The approaches range from deep learning, digital elevation map, and optical flow to mathematical models. The outputs of these techniques cover the various aspects of autonomous navigation like velocity, thrust, yaw angle, heading angle, position, and height. The paper encompasses methods for both indoor and outdoor navigation. The techniques covered mange smooth UAV navigation in different and even unfavourable illumination conditions. Furthermore, this paper serves as a medium to gain insight into the essential aspects of drone navigation methods and their applications.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Power, William, et al. Autonomous navigation for drone swarms in gps-denied environ- ments using structured learning.in IFIP International Conference on Artificial Intelligence Applications and Innovations. Springer, Cham, 2020
Akeila, Ehad, Zoran Salcic, and Akshya Swain. A self-resetting method for reducing error accumulation in INS-based tracking. in Proceedings of IEEE/ION PLANS 2010. (2010)
Lu, Yuncheng, et al. A survey on vision-based UAV navigation. Geo-spatial information science 21.1: 21–32 (2018)
Ram Prasad Padhy, Sachin Verma, Shahzad Ahmad, Suman Kumar Choudhury, Pankaj Kumar Sa. Deep Neural Network for Autonomous UAV Navigation in Indoor Corridor Environments. in Procedia Computer Science, (2018)
G. Huang, Z. Liu,K.Q Weinberger, L. van der Maaten,. Densely connected con- volutional networks, in: Proceedings of the IEEE Conference On Computer Vision and Pat- Tern Recognition, (2017b) p. 3
V. Tchernykh, M. Beck, K. Janschek, Optical Flow Navigation for an outdoor UAV using a wide-angle mono camera and DEM matching, in IFAC Proceedings Volumes, (2006)
Lukashevish, Pavel & Belotserkovsky, Alexei & Nedzved, Alexander. The new approach for reliable UAV navigation based on onboard camera image processing. 2015.
Zhang, Jun, Weisong Liu, and Yirong Wu. Novel technique for vision-based UAV navi- gation. IEEE Trans. Aerosp. Electron. Syst. 47.4: 2731-2741 (2011)
M. Demirhan, C. Premachandra, Development of an automated camera- based drone landing system. IEEE Access 8, 202111–202121 (2020)
X. Zhang, B. Xian, B. Zhao and Y. Zhang,Autonomous Flight Control of a Nano Quad- rotor Helicopter in a GPS-Denied Environment Using On-Board Vision, in IEEE Trans- actions on Industrial Electronics, vol. 62, no. 10, (Oct. 2015) pp. 6392-6403, , https://doi.org/10.1109/TIE.2015.2420036.
Miller, Alexander, et al. UAV landing based on the optical flow videonavigation. Sen- sors 19.6: 1351 (2019)
Horn, K.P. Berthold, G. Brian, Schunck. Determining optical flow. Artificial intelli- gence 17.1–3: 185–203 (1981)
Bicer, Yunus, et al. Vision-based uav guidance for autonomous landing with deep neural networks. AIAA Scitech 2019 Forum. (2019)
Amer, Karim, et al. Deep convolutional neural network based autonomous drone navigation.in Thirteenth International Conference on Machine Vision. Vol. 11605. In- ternational Society for Optics and Photonics, (2021)
Furfaro, Roberto, et al. Deep learning for autonomous lunar landing.in 2018 AAS/AIAA Astrodynamics Specialist Conference. Vol. 167. Univelt, (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Chindhe, B., Ramalingam, A., Chavan, S., Hardas, S., Patil, D. (2023). Advances in Vision-Based UAV Manoeuvring Techniques. In: Thampi, S.M., Mukhopadhyay, J., Paprzycki, M., Li, KC. (eds) International Symposium on Intelligent Informatics. ISI 2022. Smart Innovation, Systems and Technologies, vol 333. Springer, Singapore. https://doi.org/10.1007/978-981-19-8094-7_35
Download citation
DOI: https://doi.org/10.1007/978-981-19-8094-7_35
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-8093-0
Online ISBN: 978-981-19-8094-7
eBook Packages: EngineeringEngineering (R0)