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Imaging and Sensing for Unmanned Aircraft Systems: Control and Performance, Volume 1 [Kõva köide]

Edited by (Luleå University of Technology (LTU), Department of), Edited by (Universidade Federal Fluminense (UFF), Telecommunications Department, Brazil), Edited by , Edited by (Karunya University (KU), ECE Department, India), Edited by (Instituto Tecnológico de Aeronáutica (ITA), Brazil)
  • Formaat: Hardback, 360 pages, kõrgus x laius: 234x156 mm
  • Sari: Control, Robotics and Sensors
  • Ilmumisaeg: 18-Jun-2020
  • Kirjastus: Institution of Engineering and Technology
  • ISBN-10: 1785616420
  • ISBN-13: 9781785616426
Teised raamatud teemal:
Imaging and Sensing for Unmanned Aircraft Systems: Control and Performance, Volume 1Suurem pilt
  • Formaat: Hardback, 360 pages, kõrgus x laius: 234x156 mm
  • Sari: Control, Robotics and Sensors
  • Ilmumisaeg: 18-Jun-2020
  • Kirjastus: Institution of Engineering and Technology
  • ISBN-10: 1785616420
  • ISBN-13: 9781785616426
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781785616426.html
Märksõnad:
This two-volume book set explores how sensors and computer vision technologies are used for the navigation, control, stability, reliability, guidance, fault detection, self-maintenance, strategic re-planning and reconfiguration of unmanned aircraft systems (UAS).



Volume 1 concentrates on UAS control and performance methodologies including Computer Vision and Data Storage, Integrated Optical Flow for Detection and Avoidance Systems, Navigation and Intelligence, Modeling and Simulation, Multisensor Data Fusion, Vision in Micro-Aerial Vehicles (MAVs), Computer Vision in UAV using ROS, Security Aspects of UAV and Robot Operating System, Vision in Indoor and Outdoor Drones, Sensors and Computer Vision, and Small UAV for Persistent Surveillance.



Volume 2 focuses on UAS deployment and applications including UAV-CPSs as a Testbed for New Technologies and a Primer to Industry 5.0, Human-Machine Interface Design, Open Source Software (OSS) and Hardware (OSH), Image Transmission in MIMO-OSTBC System, Image Database, Communications Requirements, Video Streaming, and Communications Links, Multispectral vs Hyperspectral Imaging, Aerial Imaging and Reconstruction of Infrastructures, Deep Learning as an Alternative to Super Resolution Imaging, and Quality of Experience (QoE) and Quality of Service (QoS).
About the editors xi
Preface xiii
1 Introduction to advances in U A V avionics for imaging and sensing
1(22)
Vania V. Estrela
Jude Hemanth
Osamu Saotome
George Nikolakopoulos
Roberto Sabatini
1.1 Basic concepts
1(3)
1.2 Navigation and intelligence
4(2)
1.3 Communications
6(1)
1.4 Sensors
7(2)
1.5 Computational aspects: image/video processing, computer graphics, modelling, and visualisation
9(2)
1.6 Security, health, and standards
11(1)
1.7 Applications
12(1)
1.8 Book organization
13(10)
References
17(6)
2 Computer vision and data storage in UAVs
23(24)
Vania V. Estrela
Jude Hemanth
Hermes J. Loschi
Douglas A. Nascimento
Yuzo Iano
Navid Razmjooy
2.1 Introduction
23(4)
2.1.1 Requirements
25(1)
2.1.1 Root file system
26(1)
2.1.1 Datalogging
26(1)
2.1.1 Cloud support and virtualisation
27(1)
2.2 The architecture of the cloud-based UAV cyber-physical system
27(3)
2.3 UAV needs versus memory use
30(2)
2.3.3 Limitations of OVP
31(1)
2.3.3 General solutions and their viability analysis
32(1)
2.4 UAV data logging
32(2)
2.5 Types of data logging
34(3)
2.5.5 Requirements and recommended solutions
36(1)
2.5.5 Internal RAM with SD
36(1)
2.5.5 External RAM with SD
37(1)
2.5.5 External flash memory
37(1)
2.6 Discussion and future trends
37(4)
2.6.6 UAV-based data storage
37(1)
2.6.6 UAV-based data processing
38(1)
2.6.6 Distributed versus centralised control
38(1)
2.6.6 Impact of big data in UAV-CPSs
38(2)
2.6.6 Challenges related to privacy and the protection of personal information
40(1)
2.6.6 Organisational and cultural barriers
40(1)
2.7 Conclusions
41(6)
References
42(5)
3 Integrated optical flow for situation awareness, detection and avoidance systems in UAV systems
47(28)
William Sanchez Farfan
Osamu Saotome
Vania V. Estrela
Navid Razmjooy
3.1 Introduction
47(2)
3.2 Computer vision
49(6)
3.2.2 Optical Flow
50(5)
3.3 Optical flow and remote sensing
55(2)
3.3.3 Aerial Triangulation
56(1)
3.4 Optical flow and situational awareness
57(3)
3.4.4 Detect and avoidance system
58(2)
3.5 Optical flow and navigation by images
60(3)
3.5.5 Egomotion
61(2)
3.6 Case study: INS using FPGA
63(5)
3.6.6 Architectural proposals
65(2)
3.6.6 Integration INS/GPS/OF using a Kalman filter
67(1)
3.7 Future trends and discussion
68(2)
3.7.7 3D optical flow
68(1)
3.7.7 Multispectral and hyperspectral images
69(1)
3.8 Conclusion
70(5)
References
71(4)
4 Introduction to navigation and intelligence for UAVs relying on computer vision
75(26)
Suraj Bijjahalli
Roberto Sabatini
4.1 Introduction
75(2)
4.2 Basic terminology
77(16)
4.2.2 Visual servoing
79(5)
4.2.2 Visual odometry
84(6)
4.2.2 Terrain-referenced visual navigation
90(3)
4.3 Future trends and discussion
93(1)
4.4 Conclusions
94(7)
References
94(7)
5 Modelling and simulation of UAV systems
101(22)
Narendran Muraleedharan
Daniel S. Cohen
5.1 Need for modelling and simulation
101(1)
5.1.1 Control systems design
101(1)
5.1.1 Operator training
102(1)
5.1.1 Sub-system development and testing
102(1)
5.2 History and adoption
102(2)
5.2.2 Early aviation
103(1)
5.2.2 First computerised simulations
103(1)
5.2.2 Entry of UAVs into service
104(1)
5.2.2 Commercial and consumer drones
104(1)
5.3 Modelling of UAV dynamics
104(12)
5.3.3 Model representation methods
105(1)
5.3.3 Common reference frames
106(1)
5.3.3 Representation of state variables
107(4)
5.3.3 Deriving the system equations of motion
111(4)
5.3.3 Flight physics models
115(1)
5.4 Flight dynamics simulation
116(3)
5.4.4 Integration of the equations of motion
116(3)
5.5 Conclusion
119(4)
References
119(4)
6 Multisensor data fusion for vision-based UAV navigation and guidance
123(22)
Suraj Bijjahalli
Roberto Sabatini
6.1 Introduction
123(1)
6.2 Data-fusion algorithms
124(7)
6.2.2 Extended Kalman filter
124(3)
6.2.2 Unscented Kalman filter
127(2)
6.2.2 Integration architectures
129(2)
6.3 Fusion of visual sensors
131(14)
References
142(3)
7 Vision-based UAV pose estimation
145(28)
Paulo Silva Filho
Elcio Hideiti Shiguemori
Osamu Saotome
Jairo Panetta
7.1 Introduction
145(1)
7.2 INS-GNSS drawbacks
146(3)
7.2.2 Inertial navigation systems
146(1)
7.2.2 Global navigation satellites systems
147(2)
7.3 Visual navigation: A viable alternative
149(3)
7.4 Visual navigation strategies
152(13)
7.4.4 Photogrammetry: Extracting pose information from images
152(4)
7.4.4 Template matching
156(4)
7.4.4 Landmark recognition
160(2)
7.4.4 Visual odometry
162(2)
7.4.4 Combination of methods
164(1)
7.5 Future developments on visual navigation systems
165(1)
7.6 Conclusion
166(7)
References
167(6)
8 Vision in micro-aerial vehicles
173(44)
Navid Razmjooy
Vania V. Estrela
Roberto Sabatini
8.1 Introduction
174(9)
8.1.1 Fixed-wing MAVs
174(3)
8.1.1 Rotary-wing MAVs
177(1)
8.1.1 Flapping-wing or biomimetic MAVs
178(4)
8.1.1 Hybrid MAVs
182(1)
8.2 Computer vision as a biological inspiration
183(2)
8.3 The role of sensing in MAVs
185(5)
8.3.3 Pose-estimation sensors
186(1)
8.3.3 Environmental awareness sensors
187(1)
8.3.3 Sonar ranging sensor
187(1)
8.3.3 Infrared-range sensors
188(1)
8.3.3 Thermal imaging
189(1)
8.3.3 LIDAR
189(1)
8.3.3 Cameras
190(1)
8.4 Illumination
190(1)
8.5 Navigation, pathfinding, and orientation
191(3)
8.6 Communication and polarisation-inspired machine vision applications
194(3)
8.6.6 Robot orientation and navigation
194(1)
8.6.6 Polarisation-opponent sensors
195(2)
8.7 CCD cameras and applications in machine vision
197(4)
8.8 Error modelling of environments with uncertainties
201(1)
8.9 Further work and future trends
201(3)
8.9.9 MAV challenges
202(1)
8.9.9 Proposed solutions for MAV design challenges
202(2)
8.9.9 New frontiers in sensors
204(1)
8.10 Conclusion
204(13)
References
205(12)
9 Computer vision in UAV using ROS
217(26)
Gustavo de Carvalho Bertoli
Osamu Saotome
Vania V. Estrela
9.1 Introduction
217(1)
9.2 Computer vision on ROS
218(1)
9.3 Applications
218(19)
9.3.3 OpenCV in ROS
218(11)
9.3.3 Visual navigation
229(5)
9.3.3 Setting the drone state estimation node
234(3)
9.4 Future developments and trends in ROS
237(1)
9.5 Conclusion
238(5)
References
238(5)
10 Security aspects of UAV and robot operating system
243(18)
Gustavo de Carvalho Bertoli
Osamu Saotome
10.1 Introduction
243(1)
10.2 Unmanned aerial vehicles
244(1)
10.3 ROS basic concepts
245(3)
10.4 Security UAV review
248(1)
10.5 Security ROS review
249(1)
10.6 UAV security scenarios
250(1)
10.7 Security assessment on consumer UAV operation with ROS
251(4)
10.8 Future trends
255(1)
10.9 Conclusion
255(6)
References
258(3)
11 Vision in indoor and outdoor drones
261(20)
Maik Basso
Edison Pignaton de Freitas
11.1 Computer vision in unmanned aerial vehicles
261(12)
11.1.1 Indoor environments
264(5)
11.1.1 Outdoor environments
269(4)
11.2 Other approaches handling both indoor and outdoor environments
273(2)
11.3 Conclusion
275(6)
References
276(5)
12 Sensors and computer vision as a means to monitor and maintain a UAV structural health
281(28)
Helosman Valente de Figueiredo
Osamu Saotome
Roberto Gil Annes da Silva
12.1 Introduction
282(2)
12.1.1 Case study: aeroelastic instability flutter phenomenon
282(2)
12.2 Related work
284(2)
12.2.2 Structural health monitoring
284(1)
12.2.2 Computer vision for structural health
285(1)
12.2.2 Flutter certification
285(1)
12.2.2 Computer vision and in in-flight measurements: future trends
286(1)
12.3 Signal processing on flutter certification
286(1)
12.4 Experiments and results
287(9)
12.4.4 Synthetic data 1
287(5)
12.4.4 Wind tunnel experiment
292(4)
12.5 Discussion
296(5)
12.5.5 Computer vision
298(3)
12.6 Final remarks
301(8)
References
303(6)
13 Small UAV: persistent surveillance made possible
309(24)
Ohood Al Nuaimi
Omar Almelhi
Abdulrahman Almarzooqi
Abdulla Al Saadi Al Mansoori
Slim Sayadi
Issacniwas Swamidoss
13.1 Introduction
310(1)
13.2 System view
311(6)
13.2.2 System description
311(1)
13.2.2 Hardware components
311(3)
13.2.2 Components recommendation
314(3)
13.3 Software components
317(9)
13.3.3 Camera calibration
318(1)
13.3.3 Image stitching
318(1)
13.3.3 Stabilisation
319(1)
13.3.3 Background subtraction
319(2)
13.3.3 Object tracking
321(2)
13.3.3 Geo-location pointing
323(3)
13.4 Future trends
326(1)
13.5 Conclusion
326(7)
References
326(7)
14 Conclusions
333(4)
Vania V. Estrela
Jude Hemanth
Osamu Saotome
George Nikolakopoulos
Roberto Sabatini
Index 337
Vania Estrela is a faculty/researcher at Telecommunications Department, Universidade Federal Fluminense (UFF) and a visiting scholar at UNICAMP. Her research interests include biomedical engineering, electronic instrumentation, modelling/simulation, sustainable design, multimedia, artificial intelligence, remote sensing, STEM education, environment, and digital inclusion. She has served as a reviewer for IEEE, Elsevier, ACM, IET, Springer-Verlag, and MDPI. She has extensive experience as a project manager, post-graduate advisor (M.Sc. and D.Sc.), as well as an editor of books and special issues.



Jude Hemanth is an associate professor in the ECE Department of Karunya University (KU), India. He is a member of the IEEE task force on deep learning and serves as associate editor and editorial board member for several international refereed journals.



Osamu Saotome is a professor at the Instituto Tecnológico de Aeronáutica (ITA), Brazil. He has been involved in several international research and cooperation projects with the Brazilian Air Force, INPE, IEAv (France, Sweden, USA, and Japan).



George Nikolakopoulos is a professor in robotics and automation at the Department of Computer Science, Electrical and Space Engineering at Luleå University of Technology (LTU), Sweden. He is also a member of the ARTEMIS Scientific Council of the European Commission. He has significant experience in Managing European and National R&D&I projects funded by the EU, ESA, Swedish and the Greek National Ministry of Research.



Roberto Sabatini is a professor of aerospace engineering and aviation in the School of Engineering at RMIT University (Australia) specialising in Avionics and Intelligent/Autonomous Systems for Aerospace and Defence applications. Currently, he serves as Deputy Director (Aerospace) of the Sir Lawrence Wackett Centre and Chair of the Cyber-Physical Systems Group at RMIT University. Professor Sabatini is a Fellow and Executive Member of the Institution of Engineers Australia, Fellow of the Royal Aeronautical Society, and Fellow the Royal Institute of Navigation. Throughout his career, he led numerous industry and government-funded research projects and he has authored or co-authored over 250 peer-reviewed international publications. In addition to his primary faculty duties, Professor Sabatini serves as Vice-Chair of the IEEE-AESS Avionics Systems Panel and editor for several high-impact international journals.