
Sensor Fusion Approaches for Positioning, Navigation, and Mapping
How Autonomous Vehicles and Robots Navigate in the Real World: With MATLAB Examples
- 540 pages
- English
- PDF
- Available on iOS & Android
Sensor Fusion Approaches for Positioning, Navigation, and Mapping
How Autonomous Vehicles and Robots Navigate in the Real World: With MATLAB Examples
About this 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.
Frequently asked questions
- Essential is ideal for learners and professionals who enjoy exploring a wide range of subjects. Access the Essential Library with 800,000+ trusted titles and best-sellers across business, personal growth, and the humanities. Includes unlimited reading time and Standard Read Aloud voice.
- Complete: Perfect for advanced learners and researchers needing full, unrestricted access. Unlock 1.4M+ books across hundreds of subjects, including academic and specialized titles. The Complete Plan also includes advanced features like Premium Read Aloud and Research Assistant.
Please note we cannot support devices running on iOS 13 and Android 7 or earlier. Learn more about using the app.
Information
Table of contents
- Cover
- Series Page
- Title Page
- Copyright Page
- Dedication Page
- Contents
- About the Author
- Preface
- Acknowledgment
- Chapter 1 Coordinate Systems and Motion Modeling
- Chapter 2 Signals and Systems
- Chapter 3 Sensor Fusion Methods and Algorithms
- Chapter 4 Inertial Sensors and Inertial Navigation Systems
- Chapter 5 Radio Positioning Systems
- Chapter 6 Active Ranging Sensors
- Chapter 7 Imaging Sensors
- Chapter 8 Mapping Algorithms
- Chapter 9 Case Study #1: Wheeled Platforms
- Chapter 10 Case Study #2: Aerial Vehicles
- Chapter 11 Case Study #3: AHRS and PDR
- Chapter 12 Learning-Based Fusion Methods
- Index
- EULA