Environmental Perception Technology For Unmanned Systems

Preface......Page 6
Contents......Page 9
1.1 Introduction......Page 13
1.2 Development of Unmanned Systems......Page 15
1.3.1 Sensor Technology......Page 19
1.3.2 Sensor Fusion Technology......Page 23
1.3.3 Positioning Navigation Technology......Page 24
1.3.4 Path Planning Technique......Page 25
Bibliography......Page 26
2.1 Introduction......Page 28
2.2 Millimeter-Wave Radar Concept and Characteristics......Page 29
2.3 Radar Equation......Page 31
2.4.1 Pulse System......Page 32
2.4.2 Pulse Compression......Page 34
2.4.3 Continuous Wave System......Page 36
2.4.4 MIMO System Millimeter Wave Radar......Page 44
2.5.1 Millimeter-Wave Radar Signal Processing Technology......Page 48
2.5.2.1 Millimeter-Wave Radar Point Data Processing......Page 57
2.5.2.2 Millimeter-Wave Radar Track Data Processing......Page 58
2.5.3 Millimeter-Wave Radar Imaging Technology......Page 64
2.6.1 Application of Unmanned Aerial Vehicle (UAV)......Page 68
2.6.2 Unmanned Vehicle Application......Page 71
2.6.3 Application on Unmanned Boats......Page 73
Bibliography......Page 75
3.1 Introduction......Page 77
3.2.1 Laser......Page 78
3.2.2 LiDAR Function and Principle......Page 79
3.2.3 LiDAR Characteristics......Page 80
3.3.2 Solid State Hybrid LiDAR......Page 82
3.3.3 Solid State LiDAR......Page 86
3.4.1 LiDAR Signal Processing Technology......Page 90
3.4.2.1 Point Cloud Segmentation and Automatic Identification......Page 94
3.4.2.2 Point Cloud Filter......Page 101
3.5.1 Drone Application......Page 105
3.5.2 Application in Unmanned Vehicle......Page 107
3.5.3 Application on Unmanned Surface Vehicle......Page 110
Bibliography......Page 113
4.1 Introduction......Page 114
4.2.1 Basic Concepts of Machine Vision......Page 115
4.2.2 Characteristics of Machine Vision......Page 117
4.3.1 Camera Components......Page 118
4.3.2.1 Monocular Camera Model......Page 119
4.3.2.2 Binocular Camera Model......Page 122
4.3.2.3 RGB-D Camera Model......Page 123
4.4.1.1 Object Detection......Page 124
4.4.1.3 Image Segmentation......Page 129
4.4.2 Machine Vision Based on Deep Learning......Page 133
4.5.1 Drone Application......Page 144
4.5.2 Unmanned Vehicles Application......Page 146
4.5.3 Unmanned Boats Application......Page 147
Bibliography......Page 149
5.1 Introduction......Page 151
5.2 Infrared......Page 152
5.3 Classification of Infrared Sensor......Page 153
5.4.1 Infrared Night Vision Technology......Page 155
5.4.2 Infrared Binocular Stereo Vision......Page 157
5.4.4 Target Tracking......Page 159
5.5.1 Ultrasound......Page 161
5.5.2 Ultrasonic Sensor Principle and Characteristics......Page 162
5.6.1 Basic Structure of Ultrasonic Sensor......Page 164
5.6.2 Type of Ultrasonic Sensor......Page 165
5.7.1 Anti-jamming Technology of Ultrasonic Sensor......Page 166
5.7.2 Sector Scanning Detection of the Ultrasonic Sensor......Page 168
5.8.1 Application of Ultrasonic Sensor in Unmanned Vehicle......Page 170
5.8.2 Application of Ultrasonic Sensor in UAV......Page 172
5.8.3 Application of Ultrasonic Sensor in Unmanned Boat......Page 174
Bibliography......Page 175
6.1 Introduction......Page 177
6.2.1.2 Fully Distributed Architecture......Page 178
6.2.1.3 Hybrid Architecture......Page 180
6.2.3.1 Data Level Fusion......Page 182
6.2.3.2 Feature Level Fusion......Page 184
6.3.1.1 Concept......Page 185
6.3.1.2 Advantages of Kalman Filter in Data Fusion......Page 188
6.3.2.1 Description of the Bayesian Estimation Algorithm......Page 190
6.3.3 D-S Evidence Theory......Page 192
6.3.3.1 Basic Concept......Page 193
6.3.3.2 Combination Rule of Evidence Theory......Page 194
6.3.3.3 Decision Rules for D-S Evidence Theory......Page 195
6.3.4.1 Basic Concept and Theory of Fuzzy Logic......Page 196
6.3.4.2 Multi-sensor Data Fusion Models Based on Fuzzy Evidence Theory......Page 199
6.3.5.1 Basic Concept......Page 200
6.3.5.2 Advantages of Artificial Neural Networks in Data Fusion......Page 204
6.4.1 Application in Unmanned Aerial Vehicle......Page 205
6.4.2 Application in Unmanned Vehicle......Page 206
6.4.3 Application in Unmanned Surface Vehicle......Page 208
Bibliography......Page 209
7.2 Overview......Page 211
7.3.1 Composition of GPS System......Page 213
7.3.2 Principle of GPS Positioning......Page 215
7.3.3 Differential GPS Positioning Technology......Page 217
7.4 Inertial Navigation System......Page 218
7.4.2 Classification of the Inertial Navigation System......Page 219
7.4.3 Characteristics of Inertial Navigation System......Page 220
7.4.4 Track Estimation Technology......Page 221
7.5 Integrated Navigation Positioning......Page 222
7.5.2 Tight Combination......Page 223
7.5.3 Ultra Tight Combination......Page 224
7.6 Simultaneous Localization and Mapping (SLAM)......Page 225
7.6.1 SLAM Implementation......Page 227
7.6.2 Examples of SLAM Application......Page 231
7.7.1 Application in Unmanned Vehicle Positioning and Navigation......Page 234
7.7.2 Application in Unmanned Surface Vehicle Positioning and Navigation......Page 235
7.7.3 Application in Unmanned Aerial Vehicle Positioning and Navigation......Page 237
Bibliography......Page 239
8.1 Introduction......Page 241
8.2 Overview of Path Planning......Page 242
8.3 Path Planning Algorithm......Page 243
8.3.1 A* Search Method......Page 244
8.3.2 Artificial Potential Field Method......Page 245
8.3.3 Lattices Planning Algorithm......Page 249
8.3.4 RRT Path Planning Algorithm......Page 251
8.3.5 Genetic Algorithm......Page 253
8.3.6 Ant Colony Algorithm......Page 254
8.4.1 Application in Unmanned Aerial Vehicle......Page 256
8.4.2 Application on Unmanned Vehicles......Page 257
Bibliography......Page 259