9780262201629

Probabilistic Robotics

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  • ISBN13:

    9780262201629

  • ISBN10:

    0262201623

  • Format: Hardcover
  • Copyright: 2005-09-01
  • Publisher: Mit Pr
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Summary

Probabilistic robotics is a new and growing area in robotics, concerned with perception and control in the face of uncertainty. Building on the field of mathematical statistics, probabilistic robotics endows robots with a new level of robustness in real-world situations. This book introduces the reader to a wealth of techniques and algorithms in the field. All algorithms are based on a single overarching mathematical foundation. Each chapter provides example implementations in pseudo code, detailed mathematical derivations, discussions from a practitioner's perspective, and extensive lists of exercises and class projects. The book's Web site, http://www.probabilistic-robotics.org, has additional material. The book is relevant for anyone involved in robotic software development and scientific research. It will also be of interest to applied statisticians and engineers dealing with real-world sensor data.

Author Biography

Dieter Fox is Associate Professor and Director of the Robotics and State Estimation Lab in the Department of Computer Science and Engineering at the University of Washington.

Table of Contents

Prefacep. xvii
Acknowledgmentsp. xix
Basicsp. 1
Introductionp. 3
Uncertainty in Roboticsp. 3
Probabilistic Roboticsp. 4
Implicationsp. 9
Road Mapp. 10
Teaching Probabilistic Roboticsp. 11
Bibliographical Remarksp. 11
Recursive State Estimationp. 13
Introductionp. 13
Basic Concepts in Probabilityp. 14
Robot Environment Interactionp. 19
Bayes Filtersp. 26
Representation and Computationp. 34
Summaryp. 35
Bibliographical Remarksp. 36
Exercisesp. 36
Gaussian Filtersp. 39
Introductionp. 39
The Kalman Filterp. 40
The Extended Kalman Filterp. 54
The Unscented Kalman Filterp. 65
The Information Filterp. 71
Summaryp. 79
Bibliographical Remarksp. 81
Exercisesp. 81
Nonparametric Filtersp. 85
The Histogram Filterp. 86
Binary Bayes Filters with Static Statep. 94
The Particle Filterp. 96
Summaryp. 113
Bibliographical Remarksp. 114
Exercisesp. 115
Robot Motionp. 117
Introductionp. 117
Preliminariesp. 118
Velocity Motion Modelp. 121
Odometry Motion Modelp. 132
Motion and Mapsp. 140
Summaryp. 143
Bibliographical Remarksp. 145
Exercisesp. 145
Robot Perceptionp. 149
Introductionp. 149
Mapsp. 152
Beam Models of Range Findersp. 153
Likelihood Fields for Range Findersp. 169
Correlation-Based Measurement Modelsp. 174
Feature-Based Measurement Modelsp. 176
Practical Considerationsp. 182
Summaryp. 183
Bibliographical Remarksp. 184
Exercisesp. 185
Localizationp. 189
Mobile Robot Localization: Markov and Gaussianp. 191
A Taxonomy of Localization Problemsp. 193
Markov Localizationp. 197
Illustration of Markov Localizationp. 200
EKF Localizationp. 201
Estimating Correspondencesp. 215
Multi-Hypothesis Trackingp. 218
UKF Localizationp. 220
Practical Considerationsp. 229
Summaryp. 232
Bibliographical Remarksp. 233
Exercisesp. 234
Mobile Robot Localization: Grid And Monte Carlop. 237
Introductionp. 237
Grid Localizationp. 238
Monte Carlo Localizationp. 250
Localization in Dynamic Environmentsp. 267
Practical Considerationsp. 273
Summaryp. 274
Bibliographical Remarksp. 275
Exercisesp. 276
Mappingp. 279
Occupancy Grid Mappingp. 281
Introductionp. 281
The Occupancy Grid Mapping Algorithmp. 284
Learning Inverse Measurement Modelsp. 294
Maximum A Posteriori Occupancy Mappingp. 299
Summaryp. 304
Bibliographical Remarksp. 305
Exercisesp. 307
Simultaneous Localization and Mappingp. 309
Introductionp. 309
SLAM with Extended Kalman Filtersp. 312
EKF SLAM with Unknown Correspondencesp. 323
Summaryp. 330
Bibliographical Remarksp. 332
Exercisesp. 334
The GraphSLAM Algorithmp. 337
Introductionp. 337
Intuitive Descriptionp. 340
The GraphSLAM Algorithmp. 346
Mathematical Derivation of GraphSLAMp. 353
Data Association in GraphSLAMp. 362
Efficiency Considerationp. 368
Empirical Implementationp. 370
Alternative Optimization Techniquesp. 376
Summaryp. 379
Bibliographical Remarksp. 381
Exercisesp. 382
The Sparse Extended Information Filterp. 385
Introductionp. 385
Intuitive Descriptionp. 388
The SEIF SLAM Algorithmp. 391
Mathematical Derivation of the SEIFp. 395
Sparsificationp. 398
Amortized Approximate Map Recoveryp. 402
How Sparse Should SEIFs Be?p. 405
Incremental Data Associationp. 409
Branch-and-Bound Data Associationp. 415
Practical Considerationsp. 420
Multi-Robot SLAMp. 424
Summaryp. 432
Bibliographical Remarksp. 434
Exercisesp. 435
The FastSLAM Algorithmp. 437
The Basic Algorithmp. 439
Factoring the SLAM Posteriorp. 439
FastSLAM with Known Data Associationp. 444
Improving the Proposal Distributionp. 451
Unknown Data Associationp. 457
Map Managementp. 459
The FastSLAM Algorithmsp. 460
Efficient Implementationp. 460
FastSLAM for Feature-Based Mapsp. 468
Grid-based FastSLAMp. 474
Summaryp. 479
Bibliographical Remarksp. 481
Exercisesp. 482
Planning and Controlp. 485
Markov Decision Processesp. 487
Motivationp. 487
Uncertainty in Action Selectionp. 490
Value Iterationp. 495
Application to Robot Controlp. 503
Summaryp. 507
Bibliographical Remarksp. 509
Exercisesp. 510
Partially Observable Markov Decision Processesp. 513
Motivationp. 513
An Illustrative Examplep. 515
The Finite World POMDP Algorithmp. 527
Mathematical Derivation of POMDPsp. 531
Practical Considerationsp. 536
Summaryp. 541
Bibliographical Remarksp. 542
Exercisesp. 544
Approximate POMDP Techniquesp. 547
Motivationp. 547
QMDPsp. 549
Augmented Markov Decision Processesp. 550
Monte Carlo POMDPsp. 559
Summaryp. 565
Bibliographical Remarksp. 566
Exercisesp. 566
Explorationp. 569
Introductionp. 569
Basic Exploration Algorithmsp. 571
Active Localizationp. 575
Exploration for Learning Occupancy Grid Mapsp. 580
Exploration for SLAMp. 593
Summaryp. 600
Bibliographical Remarksp. 602
Exercisesp. 604
Bibliographyp. 607
Indexp. 639
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