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.
Arvustused
'This book provides a timely, concise, and well-scoped introduction to state estimation for robotics. It complements existing textbooks by giving a balanced presentation of estimation theoretic and geometric tools and discusses how these tools can be used to solve common estimation problems arising in robotics. It also strikes an excellent balance between theory and motivating examples.' Luca Carlone, IEEE Control Systems Magazine
Muu info
A modern look at state estimation, targeted at students and practitioners of robotics, with emphasis on three-dimensional applications.
| Preface |
|
vii | |
| Acronyms and Abbreviations |
|
ix | |
| Notation |
|
xi | |
|
|
|
1 | (8) |
|
|
|
1 | (2) |
|
1.2 Sensors, Measurements, and Problem Definition |
|
|
3 | (1) |
|
1.3 How This Book Is Organized |
|
|
4 | (1) |
|
1.4 Relationship to Other Books |
|
|
5 | (4) |
|
Part I Estimation Machinery |
|
|
|
2 Primer on Probability Theory |
|
|
9 | (26) |
|
2.1 Probability Density Functions |
|
|
9 | (6) |
|
2.2 Gaussian Probability Density Functions |
|
|
15 | (17) |
|
|
|
32 | (1) |
|
|
|
33 | (1) |
|
|
|
33 | (2) |
|
3 Linear-Gaussian Estimation |
|
|
35 | (53) |
|
3.1 Batch Discrete-Time Estimation |
|
|
35 | (14) |
|
3.2 Recursive Discrete-Time Smoothing |
|
|
49 | (7) |
|
3.3 Recursive Discrete-Time Filtering |
|
|
56 | (15) |
|
3.4 Batch Continuous-Time Estimation |
|
|
71 | (14) |
|
|
|
85 | (1) |
|
|
|
85 | (3) |
|
4 Nonlinear Non-Gaussian Estimation |
|
|
88 | (57) |
|
|
|
88 | (5) |
|
4.2 Recursive Discrete-Time Estimation |
|
|
93 | (30) |
|
4.3 Batch Discrete-Time Estimation |
|
|
123 | (15) |
|
4.4 Batch Continuous-Time Estimation |
|
|
138 | (5) |
|
|
|
143 | (1) |
|
|
|
144 | (1) |
|
5 Biases, Correspondences, and Outliers |
|
|
145 | (20) |
|
5.1 Handling Input/Measurement Biases |
|
|
145 | (7) |
|
|
|
152 | (2) |
|
|
|
154 | (7) |
|
|
|
161 | (1) |
|
|
|
161 | (4) |
|
Part II Three-Dimensional Machinery |
|
|
|
6 Primer on Three-Dimensional Geometry |
|
|
165 | (40) |
|
6.1 Vectors and Reference Frames |
|
|
165 | (3) |
|
|
|
168 | (15) |
|
|
|
183 | (7) |
|
|
|
190 | (13) |
|
|
|
203 | (1) |
|
|
|
203 | (2) |
|
|
|
205 | (82) |
|
|
|
205 | (39) |
|
|
|
244 | (12) |
|
7.3 Probability and Statistics |
|
|
256 | (26) |
|
|
|
282 | (1) |
|
|
|
282 | (5) |
|
|
|
|
8 Pose Estimation Problems |
|
|
287 | (40) |
|
8.1 Point-Cloud Alignment |
|
|
287 | (21) |
|
|
|
308 | (11) |
|
8.3 Pose-Graph Relaxation |
|
|
319 | (8) |
|
9 Pose-and-Point Estimation Problems |
|
|
327 | (20) |
|
|
|
327 | (15) |
|
9.2 Simultaneous Localization and Mapping |
|
|
342 | (5) |
|
10 Continuous-Time Estimation |
|
|
347 | (12) |
|
|
|
347 | (6) |
|
10.2 Simultaneous Trajectory Estimation and Mapping |
|
|
353 | (6) |
| References |
|
359 | (6) |
| Index |
|
365 | |
Timothy D. Barfoot is a Professor at the University of Toronto Institute for Aerospace Studies (UTIAS). He has been conducting research in the area of navigation of mobile robotics for over fifteen years, both in industry and academia, for applications including space exploration, mining, military, and transportation. He has made contributions in the area of localization, mapping, planning, and control. He sits on the editorial boards of the International Journal of Robotics Research and the Journal of Field Robotics, and was the General Chair of Field and Service Robotics 2015, which was held in Toronto.
Lisainfo e-raamatute kohta