State Estimation for Robotics

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