Mobile robotic systems depend on the ability to perceive their environment, determine their location, and build up a model of their surroundings. Furthermore, when operating in the real world, these tasks must be accomplished simultaneously and in real time, while taking into account the effects of noisy sensors, incorrect models, and complex unstructured environments. This course will explore fundamental problems central to mobile robotic systems, including localization, mapping, and simultaneous localization and mapping (SLAM). Students in the course will gain hands-on experience implementing and working with fundamental and state-of-the-art algorithms and methods for solving these problems. Topics include Bayesian filtering; sensor-fusion; sensor models (for a variety of sensing modalities); and applications to autonomous marine, ground, and air vehicles.