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.
Author(s): Timothy D. Barfoot
Publisher: Cambridge University Press
Year: 2017
Language: English
Pages: 382
Contents......Page 6
Preface......Page 7
Acronyms and Abbreviations......Page 9
Notation......Page 12
1.1 A Little History......Page 14
1.2 Sensors, Measurements, and Problem Definition......Page 16
1.3 How This Book Is Organized......Page 17
1.4 Relationship to Other Books......Page 18
Part I Estimation Machinery......Page 20
2.1 Probability Density Functions......Page 22
2.2 Gaussian Probability Density Functions......Page 28
2.3 Gaussian Processes......Page 45
2.5 Exercises......Page 46
3.1 Batch Discrete-Time Estimation......Page 48
3.2 Recursive Discrete-Time Smoothing......Page 62
3.3 Recursive Discrete-Time Filtering......Page 69
3.4 Batch Continuous-Time Estimation......Page 84
3.6 Exercises......Page 98
4.1 Introduction......Page 101
4.2 Recursive Discrete-Time Estimation......Page 106
4.3 Batch Discrete-Time Estimation......Page 136
4.4 Batch Continuous-Time Estimation......Page 151
4.5 Summary......Page 156
4.6 Exercises......Page 157
5.1 Handling Input/Measurement Biases......Page 158
5.2 Data Association......Page 165
5.3 Handling Outliers......Page 167
5.5 Exercises......Page 174
Part II Three-Dimensional Machinery......Page 176
6.1 Vectors and Reference Frames......Page 178
6.2 Rotations......Page 181
6.3 Poses......Page 196
6.4 Sensor Models......Page 203
6.6 Exercises......Page 216
7.1 Geometry......Page 218
7.2 Kinematics......Page 257
7.3 Probability and Statistics......Page 269
7.5 Exercises......Page 295
Part III Applications......Page 298
8.1 Point-Cloud Alignment......Page 300
8.2 Point-Cloud Tracking......Page 321
8.3 Pose-Graph Relaxation......Page 332
9.1 Bundle Adjustment......Page 340
9.2 Simultaneous Localization and Mapping......Page 355
10.1 Motion Prior......Page 360
10.2 Simultaneous Trajectory Estimation and Mapping......Page 366
References......Page 372
Index......Page 378
