This book introduces the key topics and concepts in autonomous mobile robotics in an integrated way, where each of the functional subsystems that comprise a mobile robot are introduced. This approach joins together concepts from system theory (input/output and state-space models, estimation, control) and decision theory (stochastic optimization, dynamic programming), addressing the uncertainty inherent to autonomous robots' actions and observations. The book provides a unified probabilistic Bayesian approach to single and multiple robot localization, sensor fusion, and decision-making.