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## Integrated optical flow for situation awareness, detection and avoidance systems in UAV systems

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Optical flow (OF) plays a decisive role in visual situation awareness, detection and obstacle avoidance systems for unmanned aerial vehicles (UAVs), which are cyberphysical systems (CPSs) that interact with the environment through sensors and actuators. The use of cameras allows the integration of computer vision (CV) algorithms with the inertial navigation systems (INS). The movement of characteristics of the image fused with the dynamic of the UAVs allows us to improve the process of remoting sense, avoid obstacles or estimate the position and velocity of the UAV. In the literature, there are various algorithms to locate characteristics points between two consecutive images. However, the computation time and consumption of physical resources such as memory features are due to embedded systems. This chapter shows (i) how to integrate the movement of the pixel textures (OF) in the image with INS data, (ii) compares different algorithms to match points between consecutive images, (iii) implements a process to encounter points between consecutive images and (iv) implements a computationally less expensive and with less memory consumption algorithm. A case study about using the field-programmable gate array (FPGA) as part of the visual servoing is discussed showing how to integrate results into the CV hardware system of a UAV and addressing the need to handle issues such as multi-resolution.

Chapter Contents:

• 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

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