Imaging and Sensing for Unmanned Aircraft Systems: Control and Performance (Control, Robotics and Sensors)

Cover
Contents
About the editors
Preface
1 Introduction to advances in UAV avionics for imaging and sensing
	1.1 Basic concepts
	1.2 Navigation and intelligence
	1.3 Communications
	1.4 Sensors
	1.5 Computational aspects: image/video processing, computer graphics, modelling, and visualisation
	1.6 Security, health, and standards
	1.7 Applications
	1.8 Book organization
	References
2 Computer vision and data storage in UAVs
	2.1 Introduction
		2.1.1 Requirements
		2.1.2 Root file system
		2.1.3 Data logging
		2.1.4 Cloud support and virtualisation
	2.2 The architecture of the cloud-based UAV cyber-physical system
	2.3 UAV needs versus memory use
		2.3.1 Limitations of OVP
		2.3.2 General solutions and their viability analysis
	2.4 UAV data logging
	2.5 Types of data logging
		2.5.1 Requirements and recommended solutions
		2.5.2 Internal RAM with SD
		2.5.3 External RAM with SD
		2.5.4 External flash memory
	2.6 Discussion and future trends
		2.6.1 UAV-based data storage
		2.6.2 UAV-based data processing
		2.6.3 Distributed versus centralised control
		2.6.4 Impact of big data in UAV-CPSs
			2.6.4.1 Infrastructure readiness
			2.6.4.2 Complexity
			2.6.4.3 Privacy
			2.6.4.4 Barriers to BD processing in UAV-CPSs
		2.6.5 Challenges related to privacy and the protection of personal information
		2.6.6 Organisational and cultural barriers
	2.7 Conclusions
	References
3 Integrated optical flow for situation awareness, detection and avoidance systems in UAV systems
	3.1 Introduction
	3.2 Computer vision
		3.2.1 Optical Flow
			3.2.1.1 Methods based on the brightness gradient
			3.2.1.2 Feature extractor algorithm
	3.3 Optical flow and remote sensing
		3.3.1 Aerial Triangulation
	3.4 Optical flow and situational awareness
		3.4.1 Detect and avoidance system
			3.4.1.1 Perception
			3.4.1.2 Comprehension
			3.4.1.3 Projection
	3.5 Optical flow and navigation by images
		3.5.1 Egomotion
	3.6 Case study: INS using FPGA
		3.6.1 Architectural proposals
			3.6.1.1 Control unit (CU)
			3.6.1.2 Generation of time
			3.6.1.3 Feature points detector
			3.6.1.4 OF calculation
			3.6.1.5 Input and output component
		3.6.2 Integration INS/GPS/OF using a Kalman filter
	3.7 Future trends and discussion
		3.7.1 3D optical flow
		3.7.2 Multispectral and hyperspectral images
	3.8 Conclusion
	References
4 Introduction to navigation and intelligence for UAVs relying on computer vision
	4.1 Introduction
	4.2 Basic terminology
		4.2.1 Visual servoing
		4.2.2 Visual odometry
		4.2.3 Terrain-referenced visual navigation
	4.3 Future trends and discussion
	4.4 Conclusions
	References
5 Modelling and simulation of UAV systems
	5.1 Need for modelling and simulation
		5.1.1 Control systems design
		5.1.2 Operator training
		5.1.3 Sub-system development and testing
	5.2 History and adoption
		5.2.1 Early aviation
		5.2.2 First computerised simulations
		5.2.3 Entry of UAVs into service
		5.2.4 Commercial and consumer drones
	5.3 Modelling of UAV dynamics
		5.3.1 Model representation methods
			5.3.1.1 Differential equations
			5.3.1.2 State-space representation
		5.3.2 Common reference frames
			5.3.2.1 Inertial frame of reference
			5.3.2.2 Earth-centre frames of reference
			5.3.2.3 Navigation frame of reference
			5.3.2.4 Body frames of reference
		5.3.3 Representation of state variables
			5.3.3.1 Euler angles
			5.3.3.2 Rotation matrices
			5.3.3.3 Quaternions
		5.3.4 Deriving the system equations of motion
			5.3.4.1 Conservation of momentum
			5.3.4.2 Euler–Lagrange method
			5.3.4.3 Newton–Euler recursive method
		5.3.5 Flight physics models
			5.3.5.1 Fixed-wing flight
			5.3.5.2 Multi-rotors and VTOL
	5.4 Flight dynamics simulation
		5.4.1 Integration of the equations of motion
			5.4.1.1 Euler method
			5.4.1.2 Runga–Kutta methods
	5.5 Conclusion
	References
6 Multisensor data fusion for vision-based UAV navigation and guidance
	6.1 Introduction
	6.2 Data-fusion algorithms
		6.2.1 Extended Kalman filter
		6.2.2 Unscented Kalman filter
		6.2.3 Integration architectures
	6.3 Fusion of visual sensors
	References
7 Vision-based UAV pose estimation
	7.1 Introduction
	7.2 INS–GNSS drawbacks
		7.2.1 Inertial navigation systems
		7.2.2 Global navigation satellites systems
	7.3 Visual navigation: A viable alternative
	7.4 Visual navigation strategies
		7.4.1 Photogrammetry: Extracting pose information from images
		7.4.2 Template matching
		7.4.3 Landmark recognition
			7.4.3.1 Knowing the exact landmark
			7.4.3.2 Identifying the landmarks' classes
		7.4.4 Visual odometry
		7.4.5 Combination of methods
	7.5 Future developments on visual navigation systems
	7.6 Conclusion
	References
8 Vision in micro-aerial vehicles
	8.1 Introduction
		8.1.1 Fixed-wing MAVs
			8.1.1.1 Longitudinal dynamics
			8.1.1.2 Lateral dynamic
		8.1.2 Rotary-wing MAVs
		8.1.3 Flapping-wing or biomimetic MAVs
		8.1.4 Hybrid MAVs
	8.2 Computer vision as a biological inspiration
	8.3 The role of sensing in MAVs
		8.3.1 Pose-estimation sensors
		8.3.2 Environmental awareness sensors
		8.3.3 Sonar ranging sensor
		8.3.4 Infrared-range sensors
		8.3.5 Thermal imaging
		8.3.6 LIDAR
		8.3.7 Cameras
	8.4 Illumination
	8.5 Navigation, pathfinding, and orientation
	8.6 Communication and polarisation-inspired machine vision applications
		8.6.1 Robot orientation and navigation
		8.6.2 Polarisation-opponent sensors
	8.7 CCD cameras and applications in machine vision
	8.8 Error modelling of environments with uncertainties
	8.9 Further work and future trends
		8.9.1 MAV challenges
		8.9.2 Proposed solutions for MAV design challenges
		8.9.3 New frontiers in sensors
	8.10 Conclusion
	References
9 Computer vision in UAV using ROS
	9.1 Introduction
	9.2 Computer vision on ROS
	9.3 Applications
		9.3.1 OpenCV in ROS
			9.3.1.1 Object detection
		9.3.2 Visual navigation
			9.3.2.1 Parallel tracking and mapping (PTAM)
			9.3.2.2 ROS package –autonomous flight
			9.3.2.3 tum ardrone GUI
			9.3.2.4 PTAM UAV camera feed and navigation
		9.3.3 Setting the drone state estimation node
			9.3.3.1 Simple navigation
	9.4 Future developments and trends in ROS
	9.5 Conclusion
	References
10 Security aspects of UAV and robot operating system
	10.1 Introduction
	10.2 Unmanned aerial vehicles
	10.3 ROS basic concepts
	10.4 Security UAV review
	10.5 Security ROS review
	10.6 UAV security scenarios
	10.7 Security assessment on consumer UAV operation with ROS
	10.8 Future trends
	10.9 Conclusion
	References
11 Vision in indoor and outdoor drones
	11.1 Computer vision in unmanned aerial vehicles
		11.1.1 Indoor environments
		11.1.2 Outdoor environments
	11.2 Other approaches handling both indoor and outdoor environments
	11.3 Conclusion
	References
12 Sensors and computer vision as a means to monitor and maintain a UAV structural health
	12.1 Introduction
		12.1.1 Case study: aeroelastic instability flutter phenomenon
	12.2 Related work
		12.2.1 Structural health monitoring
		12.2.2 Computer vision for structural health
		12.2.3 Flutter certification
		12.2.4 Computer vision and in in-flight measurements: future trends
	12.3 Signal processing on flutter certification
	12.4 Experiments and results
		12.4.1 Synthetic data
			12.4.1.1 Model of the typical wing section
			12.4.1.2 Pre-processing
			12.4.1.3 Extraction of dynamic characteristics
			12.4.1.4 Results for synthetic data
		12.4.2 Wind tunnel experiment
			12.4.2.1 Experiment description
			12.4.2.2 Results for experimental data
	12.5 Discussion
		12.5.1 Computer vision
	12.6 Final remarks
	References
13 Small UAV: persistent surveillance made possible
	13.1 Introduction
	13.2 System view
		13.2.1 System description
		13.2.2 Hardware components
		13.2.3 Components recommendation
	13.3 Software components
		13.3.1 Camera calibration
		13.3.2 Image stitching
		13.3.3 Stabilization
		13.3.4 Background subtraction
		13.3.5 Object tracking
		13.3.6 Geo-location pointing
	13.4 Future trends
	13.5 Conclusion
	References
14 Conclusions
Index
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