Sensor Fusion Approaches for Positioning, Navigation, and Mapping: How Autonomous Systems Navigate in the Real World

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, Sensor Fusion Approaches for Positioning, Navigation, and Mapping 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.

Mohamed Atia, Associate Professor, Department of Systems and Computer Engineering, Carleton University, Ottawa, 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 wide range of applications including natural language processing, speech recognition, and multi-sensors positioning, navigation, and mapping systems.