State Estimation for Robotics
A key aspect of robotics today is estimating the state (e.g., position and orientation) of
a robot as it moves through the world. Most robots and autonomous vehicles depend
on noisy data from sensors such as cameras or laser rangefinders to navigate in a three-
dimensional world. This book presents common sensor models and practical advice
on how to carry out state estimation for rotations and other state variables. It covers
both classical state estimation methods, such as the Kalman filter, and important mod-
ern topics, such as batch estimation, the Bayes filter, sigmapoint and particle filters,
robust estimation for outlier rejection, and continuous-time trajectory estimation and
its connection to Gaussian-process regression. The methods are demonstrated in the
context of important applications, such as point-cloud alignment, pose-graph relax-
ation, bundle adjustment, and simultaneous localization and mapping. Students and
practitioners of robotics alike will find this a valuable resource.
dr. timothy d. barfoot (Professor, University of Toronto Institute for
Aerospace Studies – UTIAS) has been conducting research in the area of naviga-
tion of mobile robotics for more than 15 years, both in industry and academia, for
applications including space exploration, mining, military, and transportation. He has
made contributions in the areas of localization, mapping, planning, and control. He
sits on the editorial boards of theInternational Journal of Robotics Researchand the
Journal of Field Roboticsand was the General Chair of Field and Service Robotics
2015, which was held in Toronto.
StateEstimationforRobotics
TIMOTHY D. BARFOOT
University of Toronto
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State Estimation for Robotics_Timothy D. Barfoot.pdf