Robotics: Probabilistic methods for state estimation and control

Abstract

Probabilistic robotics is a new and growing area in robotics, concerned with perception and control in the face of uncertainty. Building on the field of mathematical statistics, probabilistic robotics endows robots with a new level of robustness in real-world situations. Probabilistic approaches have been discovered as one of the most powerful ways to address highly relevant problems in mobile robotics, including robot state estimation and localization. Major challenges in the context of probabilistic algorithms for mobile robot navigation lie in the questions of how to deal with highly complex state estimation problems and how to control the robot so that it efficiently carries out its task. Robots are inherently uncertain about the state of their environments. Uncertainty arises from sensor limitations, noise, and the fact that most interesting environments are - to a certain degree - unpredictable. When “guessing” a quantity from sensor data, the probabilistic approach computes a probability distribution over what might be the case in the world, instead of generating a single “best guess” only. As a result, a robot using probabilistic methods can gracefully recover from errors, handle ambiguities, and integrate sensor data in a consistent way.