Timothy D. Barfoot

State Estimation for Robotics (eBook, ePUB)

• Format: ePub

Produktbeschreibung

A key aspect of robotics today is estimating the state, such as 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, as well as important modern 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 relaxation, bundle adjustment, and simultaneous localization and mapping. Students and practitioners of robotics alike will find this a valuable resource.

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Produktdetails

• Verlag: Cambridge University Press
• Erscheinungstermin: 31. Juli 2017
• Englisch
• ISBN-13: 9781108506731
• Artikelnr.: 48846611

Autorenporträt

Timothy D. Barfoot is a Professor at the University of Toronto Institute for Aerospace Studies (UTIAS). He has been conducting research in the area of navigation of mobile robotics for over fifteen years, both in industry and academia, for applications including space exploration, mining, military, and transportation. He has made contributions in the area of localization, mapping, planning, and control. He sits on the editorial boards of the International Journal of Robotics Research and the Journal of Field Robotics, and was the General Chair of Field and Service Robotics 2015, which was held in Toronto.

Inhaltsangabe

1. Introduction
Part I. Estimation Machinery: 2. Primer on probability theory
3. Linear-Gaussian estimation
4. Nonlinear non-Gaussian estimation
5. Biases, correspondences, and outliers
Part II. Three-Dimensional Machinery: 6. Primer on three-dimensional geometry
7. Matrix lie groups
Part III. Applications: 8. Pose estimation problems
9. Pose-and-point estimation problems
10. Continuous-time estimation.