State Estimation for Robotics

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Timothy D. BarfootCambridge University Press, 7/31/2017EAN 9781107159396, ISBN10: 1107159393Hardcover, 380 pages, 26.1 x 18.3 x 2.3 cmLanguage: EnglishA key aspect of robotics toda y 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.1. IntroductionPart I. Estimation Machinery2. Primer on probability theory3. Linear-Gaussian estimation4. Nonlinear non-Gaussian estimation5. Biases, correspondences, and outliersPart II. Three-Dimensional Machinery6. Primer on three-dimensional geometry7. Matrix lie groupsPart III. Applications8. Pose estimation problems9. Pose-and-point estimation problems10. Continuous-time estimation. Show more