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. It covers both classical state estimation methods, such as the Kalman filter, and 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.

This book presents common sensor models and practical advice on how to carry out state estimation for rotations and other state variables. Most robots and autonomous vehicles depend on noisy data from sensors such as cameras or laser rangefinders to navigate in a threedimensional world. 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. 10.2 Simultaneous Trajectory Estimation and Mapping.

9.2 Simultaneous Localization and Mapping. 2.2 Gaussian Probability Density Functions.

1.2 Sensors, Measurements, and Problem Definition.

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