{"product_id":"sensor-fusion-approaches-for-positioning-navigation-and-mapping-how-autonomous-vehicles-and-robots-navigate-in-the-real-world-with-matlab-examples","title":"Sensor Fusion Approaches for Positioning, Navigation, and Mapping: How Autonomous Vehicles and Robots Navigate in the Real World: With MATLAB Examples","description":"\u003cdiv data-cel-widget=\"bookDescription_feature_div\" data-csa-c-id=\"dpt3t1-2t2uxg-b649pi-d9hvdo\" data-csa-c-is-in-initial-active-row=\"false\" data-csa-c-asin=\"1894020928\" data-csa-c-slot-id=\"bookDescription_feature_div\" data-csa-c-content-id=\"bookDescription\" data-csa-c-type=\"widget\" data-feature-name=\"bookDescription\" class=\"celwidget\" id=\"bookDescription_feature_div\"\u003e\n\u003cdiv class=\"a-expander-collapsed-height a-row a-expander-container a-spacing-base a-expander-partial-collapse-container\" data-a-expander-collapsed-height=\"140\" data-a-expander-name=\"book_description_expander\"\u003e\n\u003cdiv class=\"a-expander-content a-expander-partial-collapse-content\" aria-expanded=\"false\"\u003e\n\u003cdiv id=\"bookDescriptionBox\" itemprop=\"reviewBody\"\u003e\n\u003cdiv id=\"bookDescriptionBox\" itemprop=\"reviewBody\"\u003e\n\u003cdiv id=\"bookDescription_feature_div\" class=\"celwidget\" data-feature-name=\"bookDescription\" data-csa-c-type=\"widget\" data-csa-c-content-id=\"bookDescription\" data-csa-c-slot-id=\"bookDescription_feature_div\" data-csa-c-asin=\"3764370327\" data-csa-c-is-in-initial-active-row=\"false\" data-csa-c-id=\"5zklon-bw1u6k-z3892-tavvr7\" data-cel-widget=\"bookDescription_feature_div\"\u003e\n\u003cdiv data-a-expander-name=\"book_description_expander\" data-a-expander-collapsed-height=\"140\" class=\"a-expander-collapsed-height a-row a-expander-container a-spacing-base a-expander-partial-collapse-container\"\u003e\n\u003cdiv aria-expanded=\"false\" class=\"a-expander-content a-expander-partial-collapse-content\"\u003e\n\u003cdiv id=\"bookDescription_feature_div\" class=\"celwidget\" data-feature-name=\"bookDescription\" data-csa-c-type=\"widget\" data-csa-c-content-id=\"bookDescription\" data-csa-c-slot-id=\"bookDescription_feature_div\" data-csa-c-asin=\"007041999X\" data-csa-c-is-in-initial-active-row=\"false\" data-csa-c-id=\"sg190u-1rtisr-p8f0cr-6wwolk\" data-cel-widget=\"bookDescription_feature_div\"\u003e\n\u003cdiv data-a-expander-name=\"book_description_expander\" data-a-expander-collapsed-height=\"140\" class=\"a-expander-collapsed-height a-row a-expander-container a-spacing-base a-expander-partial-collapse-container\"\u003e\n\u003cdiv aria-expanded=\"false\" class=\"a-expander-content a-expander-partial-collapse-content\"\u003e\n\u003cdiv id=\"bookDescriptionBox\" itemprop=\"reviewBody\"\u003e\n\u003cdiv class=\"a-expander-content a-expander-partial-collapse-content a-expander-content-expanded\" aria-expanded=\"true\"\u003e\n\u003cdiv data-cel-widget=\"bookDescription_feature_div\" data-csa-c-id=\"rt8jie-q04nm6-qesdv8-ulpusl\" data-csa-c-is-in-initial-active-row=\"false\" data-csa-c-asin=\"3038215872\" data-csa-c-slot-id=\"bookDescription_feature_div\" data-csa-c-content-id=\"bookDescription\" data-csa-c-type=\"widget\" data-feature-name=\"bookDescription\" class=\"celwidget\" id=\"bookDescription_feature_div\"\u003e\n\u003cdiv class=\"a-expander-collapsed-height a-row a-expander-container a-spacing-base a-expander-partial-collapse-container\" data-a-expander-collapsed-height=\"140\" data-a-expander-name=\"book_description_expander\"\u003e\n\u003cdiv class=\"a-expander-content a-expander-partial-collapse-content\" aria-expanded=\"false\"\u003e\n\u003cdiv class=\"a-expander-content a-expander-partial-collapse-content a-expander-content-expanded\" aria-expanded=\"true\"\u003e\n\u003cdiv class=\"a-expander-content a-expander-partial-collapse-content a-expander-content-expanded\" aria-expanded=\"true\"\u003e\n\u003cdiv data-cel-widget=\"bookDescription_feature_div\" data-csa-c-id=\"94b1p9-hba8y3-ooxjzn-frtn0e\" data-csa-c-is-in-initial-active-row=\"false\" data-csa-c-asin=\"1920744983\" data-csa-c-slot-id=\"bookDescription_feature_div\" data-csa-c-content-id=\"bookDescription\" data-csa-c-type=\"widget\" data-feature-name=\"bookDescription\" class=\"celwidget\" id=\"bookDescription_feature_div\"\u003e\n\u003cdiv class=\"a-expander-collapsed-height a-row a-expander-container a-spacing-base a-expander-partial-collapse-container\" data-a-expander-collapsed-height=\"140\" data-a-expander-name=\"book_description_expander\"\u003e\n\u003cdiv class=\"a-expander-content a-expander-partial-collapse-content\" aria-expanded=\"false\"\u003e\n\u003cp\u003e\u003cb\u003eUnique exploration of the integration of multi-sensor approaches in navigation and positioning technologies.\u003c\/b\u003e\u003c\/p\u003e\n\u003cp\u003e\u003ci\u003eSensor Fusion Approaches for Positioning, Navigation, and Mapping\u003c\/i\u003e 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.\u003c\/p\u003e\n\u003cp\u003eTo 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.\u003c\/p\u003e\n\u003cp\u003eRather 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.\u003c\/p\u003e\n\u003cp\u003eWritten by a highly qualified author, \u003ci\u003eSensor Fusion Approaches for Positioning, Navigation, and Mapping\u003c\/i\u003e discusses sample topics such as:\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eMathematical background, covering linear algebra, Euclidean space, coordinate frames, rotation and transformation, quaternion, and lie groups algebra.\u003c\/li\u003e\n\u003cli\u003eKinematics 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.\u003c\/li\u003e\n\u003cli\u003eSignals and systems, covering measurements, and noise, probability concepts, random processes, signal processing, linear dynamic systems, and stochastic systems.\u003c\/li\u003e\n\u003cli\u003eTheory, 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.\u003c\/li\u003e\n\u003cli\u003eState Estimation and Sensor Fusion methods covering filtering-based methods and learning-based approaches.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003eA comprehensive introductory text on the subject, \u003ci\u003eSensor Fusion Approaches for Positioning, Navigation, and Mapping\u003c\/i\u003e 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.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbout the Author\u003c\/strong\u003e\u003cbr\u003eMohamed 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.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable of Contents\u003c\/strong\u003e\u003cbr\u003eAbout the Author xix\u003c\/p\u003e\n\u003cp\u003ePreface xxi\u003c\/p\u003e\n\u003cp\u003eAcknowledgment xxvii\u003c\/p\u003e\n\u003cp\u003e1 Coordinate Systems and Motion Modeling 1\u003c\/p\u003e\n\u003cp\u003e1.1 Introduction 1\u003c\/p\u003e\n\u003cp\u003e1.2 Points, Vectors, and Matrices 1\u003c\/p\u003e\n\u003cp\u003e1.3 Coordinate Frames and Systems 12\u003c\/p\u003e\n\u003cp\u003e1.4 Transformation Between Frames 19\u003c\/p\u003e\n\u003cp\u003e1.5 The Mathematics of Rigid Body Motion in 3D Space 26\u003c\/p\u003e\n\u003cp\u003e1.6 Problems 40\u003c\/p\u003e\n\u003cp\u003eReferences 43\u003c\/p\u003e\n\u003cp\u003e2 Signals and Systems 45\u003c\/p\u003e\n\u003cp\u003e2.1 Introduction 45\u003c\/p\u003e\n\u003cp\u003e2.2 Probability 45\u003c\/p\u003e\n\u003cp\u003e2.3 Systems 55\u003c\/p\u003e\n\u003cp\u003e2.4 Signals 71\u003c\/p\u003e\n\u003cp\u003e2.5 Random Processes 76\u003c\/p\u003e\n\u003cp\u003e2.6 Signal Processing Basics 82\u003c\/p\u003e\n\u003cp\u003e2.7 Problems 100\u003c\/p\u003e\n\u003cp\u003eReferences 102\u003c\/p\u003e\n\u003cp\u003e3 Sensor Fusion Methods and Algorithms 103\u003c\/p\u003e\n\u003cp\u003e3.1 Introduction 103\u003c\/p\u003e\n\u003cp\u003e3.2 Estimation Philosophy 103\u003c\/p\u003e\n\u003cp\u003e3.3 Gauss-Markov Process Model 104\u003c\/p\u003e\n\u003cp\u003e3.4 State Estimation 105\u003c\/p\u003e\n\u003cp\u003e3.5 Machine Learning and Artificial Intelligence 131\u003c\/p\u003e\n\u003cp\u003e3.6 Multisensor Temporal and Spatial Extrinsic Calibration 164\u003c\/p\u003e\n\u003cp\u003e3.7 Problems 165\u003c\/p\u003e\n\u003cp\u003eReferences 166\u003c\/p\u003e\n\u003cp\u003e4 Inertial Sensors and Inertial Navigation Systems 169\u003c\/p\u003e\n\u003cp\u003e4.1 Introduction 169\u003c\/p\u003e\n\u003cp\u003e4.2 Inertial Measurements 169\u003c\/p\u003e\n\u003cp\u003e4.3 Earth Gravity\/Rotation\/Curvature Effects on Inertial Measurements 174\u003c\/p\u003e\n\u003cp\u003e4.4 Inertial Measurement Unit 178\u003c\/p\u003e\n\u003cp\u003e4.5 Inertial Sensor Errors 180\u003c\/p\u003e\n\u003cp\u003e4.6 Inertial Navigation Systems (INS) 188\u003c\/p\u003e\n\u003cp\u003e4.7 Problems 201\u003c\/p\u003e\n\u003cp\u003eReferences 202\u003c\/p\u003e\n\u003cp\u003e5 Radio Positioning Systems 203\u003c\/p\u003e\n\u003cp\u003e5.1 Introduction 203\u003c\/p\u003e\n\u003cp\u003e5.2 Concept of Operation 203\u003c\/p\u003e\n\u003cp\u003e5.3 The Electromagnetic Waves and Radio Spectrum 205\u003c\/p\u003e\n\u003cp\u003e5.4 Radio Positioning Transmitter 206\u003c\/p\u003e\n\u003cp\u003e5.5 Positioning Signal Structure 20\u003c\/p\u003e\n\u003cp\u003e5.6 Receiver Signal Processing 214\u003c\/p\u003e\n\u003cp\u003e5.7 Radio Positioning Schemes 228\u003c\/p\u003e\n\u003cp\u003e5.8 Dilution of Precision (DOP) 234\u003c\/p\u003e\n\u003cp\u003e5.9 GNSS Technology 235\u003c\/p\u003e\n\u003cp\u003e5.10 GNSS Positioning Methods 24\u003c\/p\u003e\n\u003cp\u003e5.11 GPS SPS MATLAB Project Sample 259\u003c\/p\u003e\n\u003cp\u003e5.12 Sagnac Effect 261\u003c\/p\u003e\n\u003cp\u003e5.13 Problems 264\u003c\/p\u003e\n\u003cp\u003eReferences 265\u003c\/p\u003e\n\u003cp\u003e6 Active Ranging Sensors 267\u003c\/p\u003e\n\u003cp\u003e6.1 Introduction 267\u003c\/p\u003e\n\u003cp\u003e6.2 Principle of Operation of Active Ranging\/Detection 267\u003c\/p\u003e\n\u003cp\u003e6.3 Sound, Radio, and Light Detection and Ranging 273\u003c\/p\u003e\n\u003cp\u003e6.4 Signal Processing of Ranging Sensors 275\u003c\/p\u003e\n\u003cp\u003e6.5 MATLAB Implementation of FMCW Ranging Signal Processing 289\u003c\/p\u003e\n\u003cp\u003e6.6 Range-Based Odometry 290\u003c\/p\u003e\n\u003cp\u003e6.7 Problems 301\u003c\/p\u003e\n\u003cp\u003eReferences 302\u003c\/p\u003e\n\u003cp\u003e7 Imaging Sensors 303\u003c\/p\u003e\n\u003cp\u003e7.1 Introduction 303\u003c\/p\u003e\n\u003cp\u003e7.2 How Imaging Sensors Work 303\u003c\/p\u003e\n\u003cp\u003e7.3 Camera Projection Model 304\u003c\/p\u003e\n\u003cp\u003e7.4 Estimating Camera Motion from Images 308\u003c\/p\u003e\n\u003cp\u003e7.5 Visual 3D Reconstruction: Estimating Physical Points’ Location 318\u003c\/p\u003e\n\u003cp\u003e7.6 Optical Flow Estimation 319\u003c\/p\u003e\n\u003cp\u003e7.7 Stereo Camera System 32\u003c\/p\u003e\n\u003cp\u003e7.8 Camera Calibration 325\u003c\/p\u003e\n\u003cp\u003e7.9 The Fundamental Matrix 327\u003c\/p\u003e\n\u003cp\u003e7.10 Enhanced Motion Estimation from More Than Two Images 328\u003c\/p\u003e\n\u003cp\u003e7.11 Visual Odometry 331\u003c\/p\u003e\n\u003cp\u003e7.12 Problems 338\u003c\/p\u003e\n\u003cp\u003eReferences 339\u003c\/p\u003e\n\u003cp\u003e8 Mapping Algorithms 341\u003c\/p\u003e\n\u003cp\u003e8.1 Introduction 341\u003c\/p\u003e\n\u003cp\u003e8.2 Occupancy Grid Mapping 342\u003c\/p\u003e\n\u003cp\u003e8.3 MATLAB Occupancy Grid Building Example 346\u003c\/p\u003e\n\u003cp\u003e8.4 SLAM Problem and Solution Approaches 348\u003c\/p\u003e\n\u003cp\u003e8.5 Visual SLAM 363\u003c\/p\u003e\n\u003cp\u003eReferences 373\u003c\/p\u003e\n\u003cp\u003e9 Case Study #1: Wheeled Platforms 375\u003c\/p\u003e\n\u003cp\u003e9.1 Introduction 375\u003c\/p\u003e\n\u003cp\u003e9.2 Reference Frames and the Lever Arm 376\u003c\/p\u003e\n\u003cp\u003e9.3 2D System Model 378\u003c\/p\u003e\n\u003cp\u003e9.4 2D Error Model 380\u003c\/p\u003e\n\u003cp\u003e9.5 Loosely Coupled GPS Measurement Model 382\u003c\/p\u003e\n\u003cp\u003e9.6 Tightly Coupled GPS Measurement Model 386\u003c\/p\u003e\n\u003cp\u003e9.7 GPS Fusion Using Extended Kalman Filter 391\u003c\/p\u003e\n\u003cp\u003e9.8 GPS Fusion Using Particle Filter (PF) 393\u003c\/p\u003e\n\u003cp\u003e9.9 GPS Deeply Coupled Fusion 394\u003c\/p\u003e\n\u003cp\u003e9.10 Fusing Wheel Encoder Data 395\u003c\/p\u003e\n\u003cp\u003e9.11 Fusing LiDAR with 2D Inertial\/Wheel Encoder Sensors 395\u003c\/p\u003e\n\u003cp\u003e9.12 Graph SLAM Fusion of Inertial, Wheel-Odometry, and LiDAR 401\u003c\/p\u003e\n\u003cp\u003e9.13 MATLAB Project Samples for 2D Wheeled Platforms 403\u003c\/p\u003e\n\u003cp\u003e10 Case Study #2: Aerial Vehicles 415\u003c\/p\u003e\n\u003cp\u003e10.1 Introduction 415\u003c\/p\u003e\n\u003cp\u003e10.2 3D Motion System Model 415\u003c\/p\u003e\n\u003cp\u003e10.3 Fusion of GPS Measurements 424\u003c\/p\u003e\n\u003cp\u003e10.4 Fusion of Vision Measurements 426\u003c\/p\u003e\n\u003cp\u003e10.5 Fusion of RADAR Measurements 437\u003c\/p\u003e\n\u003cp\u003e10.6 MATLAB Implementation of 3D IMU\/GNSS Fusion Using EKF 441\u003c\/p\u003e\n\u003cp\u003e11 Case Study #3: AHRS and PDR 457\u003c\/p\u003e\n\u003cp\u003e11.1 Introduction 457\u003c\/p\u003e\n\u003cp\u003e11.2 Attitude and Heading Reference System (AHRS) 458\u003c\/p\u003e\n\u003cp\u003e11.3 Pedestrian Dead Reckoning (PDR) 469\u003c\/p\u003e\n\u003cp\u003e11.4 MATLAB Project Sample of an AHRS Using EKF 473\u003c\/p\u003e\n\u003cp\u003e12 Learning-Based Fusion Methods 481\u003c\/p\u003e\n\u003cp\u003e12.1 Introduction 481\u003c\/p\u003e\n\u003cp\u003e12.2 Learning-Based Approaches 482\u003c\/p\u003e\n\u003cp\u003e12.3 Formulation of Loss Function 484\u003c\/p\u003e\n\u003cp\u003e12.4 Recent Trends and Examples 488\u003c\/p\u003e\n\u003cp\u003eReferences 500\u003c\/p\u003e\n\u003cp\u003eIndex 503\u003c\/p\u003e\n\u003cp\u003e \u003c\/p\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"a-section a-spacing-small a-padding-base\"\u003e\n\u003cdiv class=\"a-row a-expander-container a-expander-extend-container\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cb\u003eBOOKREAD™ 5-STEP SATISFACTION GUARANTEE\u003c\/b\u003e\u003c\/strong\u003e\u003cbr\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003e1. No Risk, 30-Day Money-Back Guarantee. \u003cbr\u003e2. instant download. 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