Barfoot T.D. State Estimation for Robotics

Cambridge: Cambridge University Press, 2017. — 382 p.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.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
ExercisesRecursive Discrete-Time Estimation
Batch Discrete-Time Estimation
Batch Continuous-Time EstimationExercises
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