Help your robot understand its position, direction, and surroundings by combining multiple sensors into one intelligent perception system.
A robot cannot rely on a single sensor to navigate reliably. Cameras can identify objects and environments, while compasses, IMUs, GPS, LiDAR, and other sensors provide valuable information about movement and orientation. Sensor fusion allows robots to combine these different sources of data to make better decisions, improve accuracy, and navigate confidently in complex environments.
Robot Sensor Fusion is a practical guide for beginners, students, engineers, and robotics enthusiasts who want to understand how autonomous robots determine where they are and how they move through the world.
Inside this book, you will learn:
What robot sensor fusion is and why autonomous systems need itHow cameras help robots recognize features and environmentsHow compasses and IMUs measure direction and motionThe principles of combining multiple sensor inputsHow robots estimate position using localization techniquesThe basics of filtering methods such as Kalman filtersHow sensor errors, noise, and uncertainty affect robot decisionsCamera-based navigation and visual perception conceptsHow GPS, LiDAR, encoders, and other sensors improve accuracyPractical applications in drones, delivery robots, warehouse robots, and autonomous vehiclesWritten in a clear and accessible style, this book explains the concepts behind robot localization without overwhelming mathematical complexity. You will gain a strong foundation in how robots merge information from different sensors to create a reliable understanding of their location and surroundings.
Whether you are building your first autonomous robot or expanding your robotics knowledge, Robot Sensor Fusion will help you understand one of the most important technologies behind modern intelligent machines.
Teach your robot to combine information, reduce uncertainty, and navigate with confidence.