About the editors |
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xi | |
Preface |
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xiii | |
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1 Introduction to advances in U A V avionics for imaging and sensing |
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1 | (22) |
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1 | (3) |
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1.2 Navigation and intelligence |
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4 | (2) |
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6 | (1) |
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7 | (2) |
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1.5 Computational aspects: image/video processing, computer graphics, modelling, and visualisation |
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9 | (2) |
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1.6 Security, health, and standards |
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11 | (1) |
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12 | (1) |
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13 | (10) |
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17 | (6) |
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2 Computer vision and data storage in UAVs |
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23 | (24) |
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23 | (4) |
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25 | (1) |
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26 | (1) |
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26 | (1) |
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2.1.1 Cloud support and virtualisation |
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27 | (1) |
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2.2 The architecture of the cloud-based UAV cyber-physical system |
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27 | (3) |
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2.3 UAV needs versus memory use |
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30 | (2) |
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31 | (1) |
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2.3.3 General solutions and their viability analysis |
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32 | (1) |
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32 | (2) |
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2.5 Types of data logging |
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34 | (3) |
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2.5.5 Requirements and recommended solutions |
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36 | (1) |
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2.5.5 Internal RAM with SD |
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36 | (1) |
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2.5.5 External RAM with SD |
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37 | (1) |
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2.5.5 External flash memory |
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37 | (1) |
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2.6 Discussion and future trends |
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37 | (4) |
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2.6.6 UAV-based data storage |
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37 | (1) |
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2.6.6 UAV-based data processing |
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38 | (1) |
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2.6.6 Distributed versus centralised control |
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38 | (1) |
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2.6.6 Impact of big data in UAV-CPSs |
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38 | (2) |
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2.6.6 Challenges related to privacy and the protection of personal information |
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40 | (1) |
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2.6.6 Organisational and cultural barriers |
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40 | (1) |
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41 | (6) |
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42 | (5) |
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3 Integrated optical flow for situation awareness, detection and avoidance systems in UAV systems |
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47 | (28) |
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47 | (2) |
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49 | (6) |
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50 | (5) |
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3.3 Optical flow and remote sensing |
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55 | (2) |
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3.3.3 Aerial Triangulation |
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56 | (1) |
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3.4 Optical flow and situational awareness |
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57 | (3) |
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3.4.4 Detect and avoidance system |
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58 | (2) |
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3.5 Optical flow and navigation by images |
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60 | (3) |
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61 | (2) |
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3.6 Case study: INS using FPGA |
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63 | (5) |
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3.6.6 Architectural proposals |
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65 | (2) |
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3.6.6 Integration INS/GPS/OF using a Kalman filter |
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67 | (1) |
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3.7 Future trends and discussion |
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68 | (2) |
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68 | (1) |
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3.7.7 Multispectral and hyperspectral images |
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69 | (1) |
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70 | (5) |
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71 | (4) |
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4 Introduction to navigation and intelligence for UAVs relying on computer vision |
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75 | (26) |
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75 | (2) |
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77 | (16) |
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79 | (5) |
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84 | (6) |
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4.2.2 Terrain-referenced visual navigation |
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90 | (3) |
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4.3 Future trends and discussion |
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93 | (1) |
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94 | (7) |
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94 | (7) |
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5 Modelling and simulation of UAV systems |
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101 | (22) |
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5.1 Need for modelling and simulation |
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101 | (1) |
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5.1.1 Control systems design |
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101 | (1) |
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102 | (1) |
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5.1.1 Sub-system development and testing |
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102 | (1) |
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102 | (2) |
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103 | (1) |
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5.2.2 First computerised simulations |
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103 | (1) |
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5.2.2 Entry of UAVs into service |
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104 | (1) |
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5.2.2 Commercial and consumer drones |
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104 | (1) |
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5.3 Modelling of UAV dynamics |
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104 | (12) |
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5.3.3 Model representation methods |
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105 | (1) |
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5.3.3 Common reference frames |
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106 | (1) |
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5.3.3 Representation of state variables |
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107 | (4) |
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5.3.3 Deriving the system equations of motion |
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111 | (4) |
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5.3.3 Flight physics models |
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115 | (1) |
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5.4 Flight dynamics simulation |
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116 | (3) |
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5.4.4 Integration of the equations of motion |
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116 | (3) |
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119 | (4) |
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119 | (4) |
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6 Multisensor data fusion for vision-based UAV navigation and guidance |
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123 | (22) |
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123 | (1) |
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6.2 Data-fusion algorithms |
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124 | (7) |
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6.2.2 Extended Kalman filter |
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124 | (3) |
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6.2.2 Unscented Kalman filter |
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127 | (2) |
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6.2.2 Integration architectures |
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129 | (2) |
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6.3 Fusion of visual sensors |
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131 | (14) |
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142 | (3) |
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7 Vision-based UAV pose estimation |
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145 | (28) |
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145 | (1) |
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146 | (3) |
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7.2.2 Inertial navigation systems |
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146 | (1) |
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7.2.2 Global navigation satellites systems |
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147 | (2) |
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7.3 Visual navigation: A viable alternative |
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149 | (3) |
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7.4 Visual navigation strategies |
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152 | (13) |
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7.4.4 Photogrammetry: Extracting pose information from images |
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152 | (4) |
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156 | (4) |
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7.4.4 Landmark recognition |
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160 | (2) |
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162 | (2) |
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7.4.4 Combination of methods |
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164 | (1) |
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7.5 Future developments on visual navigation systems |
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165 | (1) |
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166 | (7) |
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167 | (6) |
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8 Vision in micro-aerial vehicles |
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173 | (44) |
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174 | (9) |
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174 | (3) |
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177 | (1) |
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8.1.1 Flapping-wing or biomimetic MAVs |
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178 | (4) |
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182 | (1) |
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8.2 Computer vision as a biological inspiration |
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183 | (2) |
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8.3 The role of sensing in MAVs |
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185 | (5) |
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8.3.3 Pose-estimation sensors |
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186 | (1) |
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8.3.3 Environmental awareness sensors |
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187 | (1) |
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8.3.3 Sonar ranging sensor |
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187 | (1) |
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8.3.3 Infrared-range sensors |
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188 | (1) |
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189 | (1) |
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189 | (1) |
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190 | (1) |
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190 | (1) |
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8.5 Navigation, pathfinding, and orientation |
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191 | (3) |
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8.6 Communication and polarisation-inspired machine vision applications |
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194 | (3) |
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8.6.6 Robot orientation and navigation |
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194 | (1) |
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8.6.6 Polarisation-opponent sensors |
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195 | (2) |
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8.7 CCD cameras and applications in machine vision |
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197 | (4) |
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8.8 Error modelling of environments with uncertainties |
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201 | (1) |
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8.9 Further work and future trends |
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201 | (3) |
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202 | (1) |
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8.9.9 Proposed solutions for MAV design challenges |
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202 | (2) |
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8.9.9 New frontiers in sensors |
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204 | (1) |
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204 | (13) |
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205 | (12) |
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9 Computer vision in UAV using ROS |
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217 | (26) |
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Gustavo de Carvalho Bertoli |
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217 | (1) |
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9.2 Computer vision on ROS |
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218 | (1) |
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218 | (19) |
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218 | (11) |
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229 | (5) |
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9.3.3 Setting the drone state estimation node |
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234 | (3) |
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9.4 Future developments and trends in ROS |
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237 | (1) |
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238 | (5) |
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238 | (5) |
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10 Security aspects of UAV and robot operating system |
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243 | (18) |
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Gustavo de Carvalho Bertoli |
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243 | (1) |
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10.2 Unmanned aerial vehicles |
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244 | (1) |
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245 | (3) |
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248 | (1) |
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249 | (1) |
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10.6 UAV security scenarios |
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250 | (1) |
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10.7 Security assessment on consumer UAV operation with ROS |
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251 | (4) |
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255 | (1) |
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255 | (6) |
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258 | (3) |
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11 Vision in indoor and outdoor drones |
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261 | (20) |
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Edison Pignaton de Freitas |
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11.1 Computer vision in unmanned aerial vehicles |
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261 | (12) |
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11.1.1 Indoor environments |
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264 | (5) |
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11.1.1 Outdoor environments |
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269 | (4) |
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11.2 Other approaches handling both indoor and outdoor environments |
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273 | (2) |
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275 | (6) |
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276 | (5) |
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12 Sensors and computer vision as a means to monitor and maintain a UAV structural health |
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281 | (28) |
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Helosman Valente de Figueiredo |
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Roberto Gil Annes da Silva |
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282 | (2) |
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12.1.1 Case study: aeroelastic instability flutter phenomenon |
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282 | (2) |
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284 | (2) |
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12.2.2 Structural health monitoring |
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284 | (1) |
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12.2.2 Computer vision for structural health |
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285 | (1) |
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12.2.2 Flutter certification |
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285 | (1) |
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12.2.2 Computer vision and in in-flight measurements: future trends |
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286 | (1) |
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12.3 Signal processing on flutter certification |
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286 | (1) |
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12.4 Experiments and results |
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287 | (9) |
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287 | (5) |
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12.4.4 Wind tunnel experiment |
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292 | (4) |
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296 | (5) |
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298 | (3) |
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301 | (8) |
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303 | (6) |
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13 Small UAV: persistent surveillance made possible |
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309 | (24) |
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Abdulla Al Saadi Al Mansoori |
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310 | (1) |
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311 | (6) |
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13.2.2 System description |
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311 | (1) |
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13.2.2 Hardware components |
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311 | (3) |
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13.2.2 Components recommendation |
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314 | (3) |
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317 | (9) |
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13.3.3 Camera calibration |
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318 | (1) |
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318 | (1) |
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319 | (1) |
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13.3.3 Background subtraction |
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319 | (2) |
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321 | (2) |
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13.3.3 Geo-location pointing |
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323 | (3) |
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326 | (1) |
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326 | (7) |
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326 | (7) |
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333 | (4) |
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Index |
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