About the Book
Unique exploration of the integration of multi-sensor approaches in navigation and positioning technologies.
Sensor Fusion Approaches for Positioning, Navigation, and Mapping discusses the fundamental concepts and practical implementation of sensor fusion in positioning and mapping technology, explaining the integration of inertial sensors, radio positioning systems, visual sensors, depth sensors, radar measurements, and LiDAR measurements. The book includes case studies on ground wheeled vehicles, drones, and wearable devices to demonstrate the presented concepts.
To aid in reader comprehension and provide readers with hands-on training in sensor fusion, pedagogical features are included throughout the text: block diagrams, photographs, plot graphs, examples, solved problems, case studies, sample codes with instruction manuals, and guided tutorials.
Rather than simply addressing a specific sensor or problem domain without much focus on the big picture of sensor fusion and integration, the book utilizes a holistic and comprehensive approach to enable readers to fully grasp interrelated concepts.
Written by a highly qualified author, Sensor Fusion Approaches for Positioning, Navigation, and Mapping discusses sample topics such as:
Mathematical background, covering linear algebra, Euclidean space, coordinate frames, rotation and transformation, quaternion, and lie groups algebra.
Kinematics of rigid platforms in 3D space, covering motion modeling in rotating and non-rotating frames and under gravity field, and different representations of position, velocity, and orientation.
Signals and systems, covering measurements, and noise, probability concepts, random processes, signal processing, linear dynamic systems, and stochastic systems.
Theory, measurements, and signal processing of state-of-the-art positioning and mapping sensors/systems covering inertial sensors, radio positioning systems, ranging and detection sensors, and imaging sensors.
State Estimation and Sensor Fusion methods covering filtering-based methods and learning-based approaches.
A comprehensive introductory text on the subject, Sensor Fusion Approaches for Positioning, Navigation, and Mapping enables students to grasp the fundamentals of the subject and support their learning via ample pedagogical features. Practicing robotics and navigation systems engineers can implement included sensor fusion algorithms on practical platforms.
Table of Contents:
About the Author xix
Preface xxi
Acknowledgment xxvii
1 Coordinate Systems and Motion Modeling 1
1.1 Introduction 1
1.2 Points, Vectors, and Matrices 1
1.3 Coordinate Frames and Systems 12
1.4 Transformation Between Frames 19
1.5 The Mathematics of Rigid Body Motion in 3D Space 26
1.6 Problems 40
References 43
2 Signals and Systems 45
2.1 Introduction 45
2.2 Probability 45
2.3 Systems 55
2.4 Signals 71
2.5 Random Processes 76
2.6 Signal Processing Basics 82
2.7 Problems 100
References 102
3 Sensor Fusion Methods and Algorithms 103
3.1 Introduction 103
3.2 Estimation Philosophy 103
3.3 Gauss-Markov Process Model 104
3.4 State Estimation 105
3.5 Machine Learning and Artificial Intelligence 131
3.6 Multisensor Temporal and Spatial Extrinsic Calibration 164
3.7 Problems 165
References 166
4 Inertial Sensors and Inertial Navigation Systems 169
4.1 Introduction 169
4.2 Inertial Measurements 169
4.3 Earth Gravity/Rotation/Curvature Effects on Inertial Measurements 174
4.4 Inertial Measurement Unit 178
4.5 Inertial Sensor Errors 180
4.6 Inertial Navigation Systems (INS) 188
4.7 Problems 201
References 202
5 Radio Positioning Systems 203
5.1 Introduction 203
5.2 Concept of Operation 203
5.3 The Electromagnetic Waves and Radio Spectrum 205
5.4 Radio Positioning Transmitter 206
5.5 Positioning Signal Structure 20
5.6 Receiver Signal Processing 214
5.7 Radio Positioning Schemes 228
5.8 Dilution of Precision (DOP) 234
5.9 GNSS Technology 235
5.10 GNSS Positioning Methods 24
5.11 GPS SPS MATLAB Project Sample 259
5.12 Sagnac Effect 261
5.13 Problems 264
References 265
6 Active Ranging Sensors 267
6.1 Introduction 267
6.2 Principle of Operation of Active Ranging/Detection 267
6.3 Sound, Radio, and Light Detection and Ranging 273
6.4 Signal Processing of Ranging Sensors 275
6.5 MATLAB Implementation of FMCW Ranging Signal Processing 289
6.6 Range-Based Odometry 290
6.7 Problems 301
References 302
7 Imaging Sensors 303
7.1 Introduction 303
7.2 How Imaging Sensors Work 303
7.3 Camera Projection Model 304
7.4 Estimating Camera Motion from Images 308
7.5 Visual 3D Reconstruction: Estimating Physical Points’ Location 318
7.6 Optical Flow Estimation 319
7.7 Stereo Camera System 32
7.8 Camera Calibration 325
7.9 The Fundamental Matrix 327
7.10 Enhanced Motion Estimation from More Than Two Images 328
7.11 Visual Odometry 331
7.12 Problems 338
References 339
8 Mapping Algorithms 341
8.1 Introduction 341
8.2 Occupancy Grid Mapping 342
8.3 MATLAB Occupancy Grid Building Example 346
8.4 SLAM Problem and Solution Approaches 348
8.5 Visual SLAM 363
References 373
9 Case Study #1: Wheeled Platforms 375
9.1 Introduction 375
9.2 Reference Frames and the Lever Arm 376
9.3 2D System Model 378
9.4 2D Error Model 380
9.5 Loosely Coupled GPS Measurement Model 382
9.6 Tightly Coupled GPS Measurement Model 386
9.7 GPS Fusion Using Extended Kalman Filter 391
9.8 GPS Fusion Using Particle Filter (PF) 393
9.9 GPS Deeply Coupled Fusion 394
9.10 Fusing Wheel Encoder Data 395
9.11 Fusing LiDAR with 2D Inertial/Wheel Encoder Sensors 395
9.12 Graph SLAM Fusion of Inertial, Wheel-Odometry, and LiDAR 401
9.13 MATLAB Project Samples for 2D Wheeled Platforms 403
10 Case Study #2: Aerial Vehicles 415
10.1 Introduction 415
10.2 3D Motion System Model 415
10.3 Fusion of GPS Measurements 424
10.4 Fusion of Vision Measurements 426
10.5 Fusion of RADAR Measurements 437
10.6 MATLAB Implementation of 3D IMU/GNSS Fusion Using EKF 441
11 Case Study #3: AHRS and PDR 457
11.1 Introduction 457
11.2 Attitude and Heading Reference System (AHRS) 458
11.3 Pedestrian Dead Reckoning (PDR) 469
11.4 MATLAB Project Sample of an AHRS Using EKF 473
12 Learning-Based Fusion Methods 481
12.1 Introduction 481
12.2 Learning-Based Approaches 482
12.3 Formulation of Loss Function 484
12.4 Recent Trends and Examples 488
References 500
Index 503
About the Author :
Mohamed M. Atia, Associate Professor, Department of Systems and Computer Engineering, Carleton University, Ottawa, Ontario, Canada. Dr. Atia received a PhD in Electrical and Computer Engineering from Queen’s University at Kingston in 2013. Before joining academia, he worked in industry developing signal processing and artificial intelligence algorithms for a wide range of applications including natural language processing, speech recognition, and multi-sensors positioning, navigation, and mapping systems.
