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barfoot t state estimation robotics 2017

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Textbook in PDF format 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. Contents: Preface Acronyms and Abbreviations Notation A Little History Sensors, Measurements, and Problem Definition How This Book Is Organized Relationship to Other Books Estimation Machinery Probability Density Functions Gaussian Probability Density Functions Gaussian Processes Exercises Batch Discrete-Time Estimation Recursive Discrete-Time Smoothing Recursive Discrete-Time Filtering Batch Continuous-Time Estimation Exercises Introduction Recursive Discrete-Time Estimation Batch Discrete-Time Estimation Batch Continuous-Time Estimation Summary Exercises Handling Input/Measurement Biases Data Association Handling Outliers Exercises Three-Dimensional Machinery Vectors and Reference Frames Rotations Poses Sensor Models Exercises Geometry Kinematics Probability and Statistics Exercises Applications Point-Cloud Alignment Point-Cloud Tracking Pose-Graph Relaxation Bundle Adjustment Simultaneous Localization and Mapping Motion Prior Simultaneous Trajectory Estimation and Mapping References Index

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Barfoot T. State Estimation for Robotics 2017.pdf 6.4 MB

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barfoot t state estimation robotics 2017

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