Note: Supplemental materials are not guaranteed with Rental or Used book purchases.
Purchase Benefits
What is included with this book?
Preface | p. xvii |
Acknowledgments | p. xix |
Basics | p. 1 |
Introduction | p. 3 |
Uncertainty in Robotics | p. 3 |
Probabilistic Robotics | p. 4 |
Implications | p. 9 |
Road Map | p. 10 |
Teaching Probabilistic Robotics | p. 11 |
Bibliographical Remarks | p. 11 |
Recursive State Estimation | p. 13 |
Introduction | p. 13 |
Basic Concepts in Probability | p. 14 |
Robot Environment Interaction | p. 19 |
Bayes Filters | p. 26 |
Representation and Computation | p. 34 |
Summary | p. 35 |
Bibliographical Remarks | p. 36 |
Exercises | p. 36 |
Gaussian Filters | p. 39 |
Introduction | p. 39 |
The Kalman Filter | p. 40 |
The Extended Kalman Filter | p. 54 |
The Unscented Kalman Filter | p. 65 |
The Information Filter | p. 71 |
Summary | p. 79 |
Bibliographical Remarks | p. 81 |
Exercises | p. 81 |
Nonparametric Filters | p. 85 |
The Histogram Filter | p. 86 |
Binary Bayes Filters with Static State | p. 94 |
The Particle Filter | p. 96 |
Summary | p. 113 |
Bibliographical Remarks | p. 114 |
Exercises | p. 115 |
Robot Motion | p. 117 |
Introduction | p. 117 |
Preliminaries | p. 118 |
Velocity Motion Model | p. 121 |
Odometry Motion Model | p. 132 |
Motion and Maps | p. 140 |
Summary | p. 143 |
Bibliographical Remarks | p. 145 |
Exercises | p. 145 |
Robot Perception | p. 149 |
Introduction | p. 149 |
Maps | p. 152 |
Beam Models of Range Finders | p. 153 |
Likelihood Fields for Range Finders | p. 169 |
Correlation-Based Measurement Models | p. 174 |
Feature-Based Measurement Models | p. 176 |
Practical Considerations | p. 182 |
Summary | p. 183 |
Bibliographical Remarks | p. 184 |
Exercises | p. 185 |
Localization | p. 189 |
Mobile Robot Localization: Markov and Gaussian | p. 191 |
A Taxonomy of Localization Problems | p. 193 |
Markov Localization | p. 197 |
Illustration of Markov Localization | p. 200 |
EKF Localization | p. 201 |
Estimating Correspondences | p. 215 |
Multi-Hypothesis Tracking | p. 218 |
UKF Localization | p. 220 |
Practical Considerations | p. 229 |
Summary | p. 232 |
Bibliographical Remarks | p. 233 |
Exercises | p. 234 |
Mobile Robot Localization: Grid And Monte Carlo | p. 237 |
Introduction | p. 237 |
Grid Localization | p. 238 |
Monte Carlo Localization | p. 250 |
Localization in Dynamic Environments | p. 267 |
Practical Considerations | p. 273 |
Summary | p. 274 |
Bibliographical Remarks | p. 275 |
Exercises | p. 276 |
Mapping | p. 279 |
Occupancy Grid Mapping | p. 281 |
Introduction | p. 281 |
The Occupancy Grid Mapping Algorithm | p. 284 |
Learning Inverse Measurement Models | p. 294 |
Maximum A Posteriori Occupancy Mapping | p. 299 |
Summary | p. 304 |
Bibliographical Remarks | p. 305 |
Exercises | p. 307 |
Simultaneous Localization and Mapping | p. 309 |
Introduction | p. 309 |
SLAM with Extended Kalman Filters | p. 312 |
EKF SLAM with Unknown Correspondences | p. 323 |
Summary | p. 330 |
Bibliographical Remarks | p. 332 |
Exercises | p. 334 |
The GraphSLAM Algorithm | p. 337 |
Introduction | p. 337 |
Intuitive Description | p. 340 |
The GraphSLAM Algorithm | p. 346 |
Mathematical Derivation of GraphSLAM | p. 353 |
Data Association in GraphSLAM | p. 362 |
Efficiency Consideration | p. 368 |
Empirical Implementation | p. 370 |
Alternative Optimization Techniques | p. 376 |
Summary | p. 379 |
Bibliographical Remarks | p. 381 |
Exercises | p. 382 |
The Sparse Extended Information Filter | p. 385 |
Introduction | p. 385 |
Intuitive Description | p. 388 |
The SEIF SLAM Algorithm | p. 391 |
Mathematical Derivation of the SEIF | p. 395 |
Sparsification | p. 398 |
Amortized Approximate Map Recovery | p. 402 |
How Sparse Should SEIFs Be? | p. 405 |
Incremental Data Association | p. 409 |
Branch-and-Bound Data Association | p. 415 |
Practical Considerations | p. 420 |
Multi-Robot SLAM | p. 424 |
Summary | p. 432 |
Bibliographical Remarks | p. 434 |
Exercises | p. 435 |
The FastSLAM Algorithm | p. 437 |
The Basic Algorithm | p. 439 |
Factoring the SLAM Posterior | p. 439 |
FastSLAM with Known Data Association | p. 444 |
Improving the Proposal Distribution | p. 451 |
Unknown Data Association | p. 457 |
Map Management | p. 459 |
The FastSLAM Algorithms | p. 460 |
Efficient Implementation | p. 460 |
FastSLAM for Feature-Based Maps | p. 468 |
Grid-based FastSLAM | p. 474 |
Summary | p. 479 |
Bibliographical Remarks | p. 481 |
Exercises | p. 482 |
Planning and Control | p. 485 |
Markov Decision Processes | p. 487 |
Motivation | p. 487 |
Uncertainty in Action Selection | p. 490 |
Value Iteration | p. 495 |
Application to Robot Control | p. 503 |
Summary | p. 507 |
Bibliographical Remarks | p. 509 |
Exercises | p. 510 |
Partially Observable Markov Decision Processes | p. 513 |
Motivation | p. 513 |
An Illustrative Example | p. 515 |
The Finite World POMDP Algorithm | p. 527 |
Mathematical Derivation of POMDPs | p. 531 |
Practical Considerations | p. 536 |
Summary | p. 541 |
Bibliographical Remarks | p. 542 |
Exercises | p. 544 |
Approximate POMDP Techniques | p. 547 |
Motivation | p. 547 |
QMDPs | p. 549 |
Augmented Markov Decision Processes | p. 550 |
Monte Carlo POMDPs | p. 559 |
Summary | p. 565 |
Bibliographical Remarks | p. 566 |
Exercises | p. 566 |
Exploration | p. 569 |
Introduction | p. 569 |
Basic Exploration Algorithms | p. 571 |
Active Localization | p. 575 |
Exploration for Learning Occupancy Grid Maps | p. 580 |
Exploration for SLAM | p. 593 |
Summary | p. 600 |
Bibliographical Remarks | p. 602 |
Exercises | p. 604 |
Bibliography | p. 607 |
Index | p. 639 |
Table of Contents provided by Ingram. All Rights Reserved. |
The New copy of this book will include any supplemental materials advertised. Please check the title of the book to determine if it should include any access cards, study guides, lab manuals, CDs, etc.
The Used, Rental and eBook copies of this book are not guaranteed to include any supplemental materials. Typically, only the book itself is included. This is true even if the title states it includes any access cards, study guides, lab manuals, CDs, etc.