Recognition of a landing platform for unmanned aerial vehicles by using computer vision-based techniques |
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| Affiliation: | 1. Dept. Computer Science and Automatic Control, National University of Distance Education, 28040 Madrid, Spain;2. Dept. Software Engineering and Artificial Intelligence, Faculty of Computer Science, Complutense University, 28040 Madrid, Spain;3. Dept. of Computer Architecture and Automatic, Faculty of Physics, University Complutense of Madrid 28040 Madrid, Spain;1. Young Researchers and Elite Club, Qazvin Branch, Islamic Azad University, Qazvin, Iran;2. School of Science, Technology, Engineering and Mathematics (STEM), University of Washington, Bothell, USA;3. Department of Computer Science, College of Science, University of Tehran, Tehran, Iran;1. PPGC, Instituto de Informática, Universidade Federal do Rio Grande do Sul, Campus do Vale, Porto Alegre - RS, 91501-970, Brazil;2. Colégio Politécnico, Universidade Federal de Santa Maria, Av. Roraima, 1000 – Campus UFSM, Santa Maria – RS, 97105-900, Brazil;3. PPGI, Universidade Federal de Santa Maria, Av. Roraima, 1000 – Campus UFSM, Santa Maria – RS, 97105-900, Brazil;1. Department of CSE, Indian Institute of Technology Roorkee, India;2. Department of ECE, Institute of Engineering & Management, Kolkata, India;3. Department of CSE, Institute of Engineering & Management, Kolkata, India;4. CVPR Unit, Indian Statistical Institute, Kolkata, India;1. Faculty of Engineering, Burapha University, Chonburi, Thailand;2. Department of Industrial, Manufacturing, and Systems Engineering, The University of Texas at Arlington, U.S.A.;3. School of Industrial Management Engineering, Korea University, Seoul, Korea;1. Univ. Grenoble Alpes, LIG, F-38000 Grenoble, France CNRS, LIG, F-38000 Grenoble, France;2. Univ. Grenoble Alpes, G-SCOP, F-38000 Grenoble, France CNRS, G-SCOP, F-38000 Grenoble, France;3. STMicroelectronics, 850 rue Jean Monnet, Grenoble, France;1. UC3M-BS Institute of Financial Big Data, Universidad Carlos III de Madrid, Getafe, Spain;2. School of Computing and Engineering, University of West London, London, United Kingdom;3. Facultad de Informatica, Universidad Complutense de Madrid, Madrid, Spain |
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| Abstract: | The use of Unmanned Aerial Vehicles (UAVs) is growing significantly for many and varied purposes. During the mission, an outdoor UAV is guided by following the planned path using GPS signals. However, the GPS capability may become defective or the environment may be GPS-denied, and an additional safety aid is therefore required for the automatic landing phase that is independent of GPS data. Most UAVs are equipped with machine vision systems which, together with onboard analysis, can be used for safe, automatic landing. This contributes greatly to the overall success of autonomous flight.This paper proposes an automatic expert system, based on image segmentation procedures, that assists safe landing through recognition and relative orientation of the UAV and platform. The proposed expert system exploits the human experience that has been incorporated into the machine vision system, which is mapped into the proposed image processing modules. The result is an improved reliability capability that could be incorporated into any UAV, and is especially robust for rotary wing UAVs. This is clearly a desirable fail-safe capability. |
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