Applied Probabilistic Robotics provides a practical and comprehensive guide to the use of probability theory in designing intelligent and reliable robotic systems. The book explores how robots use uncertain information from sensors, environments, and internal models to make decisions, navigate spaces, and perform complex tasks effectively. Covering key concepts such as probabilistic localization, mapping, sensor fusion, motion planning, and decision-making under uncertainty, this book explains the mathematical foundations and real-world applications of modern robotic technologies. It highlights how probabilistic methods enable robots to operate in unpredictable environments while improving accuracy, adaptability, and autonomy. Designed for students, researchers, engineers, and robotics professionals, Applied Probabilistic Robotics bridges theory and practice by presenting essential algorithms, models, and techniques used in autonomous vehicles, mobile robots, and intelligent robotic systems. This handbook serves as a valuable resource for understanding how uncertainty can be managed to create smarter, safer, and more capable robots.