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 |
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