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  3. State Estimation for Robotics
  • Cited by 447
Publisher:
Cambridge University Press
Online publication date:
August 2017
Print publication year:
2017
Online ISBN:
9781316671528
DOI:
https://doi.org/10.1017/9781316671528
Subjects:
Engineering, Engineering Design, Kinematics, and Robotics, Computer Science, Computer Graphics, Image Processing and Robotics
Information

Book description

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.

Reviews

‘This book provides a timely, concise, and well-scoped introduction to state estimation for robotics. It complements existing textbooks by giving a balanced presentation of estimation theoretic and geometric tools and discusses how these tools can be used to solve common estimation problems arising in robotics. It also strikes an excellent balance between theory and motivating examples.’

Luca Carlone Source: IEEE Control Systems Magazine

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Contents

Contents
  • 1 - Introduction
    pp 1-6
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References
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