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Probabilistic Robotics下载
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2019-09-24 03:00:52
卡尔曼滤波原理经典书籍,结合机器人相关应用
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Probabilistic Robotics下载
卡尔曼滤波原理经典书籍,结合机器人相关应用 相关下载链接://download.csdn.net/download/evefu710/9818507?utm_source=bbsseo
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Pro
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Robot
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- 2005
卡耐基人手一本的
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必备书籍
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Robot
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- 2005。不用再购买书了!
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Pro
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( PPT+PDF)
《
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》高清英文PDF_概率机器人_控制自动化_
概率机器人学是机器人学中相对较新的方向,它致力于研究机器人感知和行为的不确定性。概率机器人的主要思想就是用概率理论的运算去明确地表示这种不确定性,换句话说,不再只依赖可能出现的情况的单一的“最好推测“而是用概率算法来表示在整个推测空间的概率分布信息。以数学上合理的方式来表示模糊性和置信度。然后根据存在的不确定性选择相对鲁棒的控制方式。
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PRO
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一本机器人的好书
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.pdf
英文版高清带书签 Contents Preface xvii Acknowledgments xix I Bas
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s 1 1 Introduction 3 1.1 Uncertainty in
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3 1.2
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4 1.3 Impl
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ations 9 1.4 Road Map 10 1.5 Teaching
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11 1.6 Bibliograph
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al Remarks 11 2 Recursive State Estimation 13 2.1 Introduction 13 2.2 Bas
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Concepts in
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Generative Laws 24 2.3.4 Belief Distributions 25 2.4 Bayes Filters 26 2.4.1 The Bayes Filter Algorithm 26 2.4.2 Example 28 2.4.3 Mathemat
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al Derivation of the Bayes Filter 31 2.4.4 The Markov Assumption 33 2.5 Representation and Computation 34 2.6 Summary 35 2.7 Bibliograph
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al Remarks 36 2.8 Exercises 36 3 Gaussian Filters 39 3.1 Introduction 39 3.2 The Kalman Filter 40 3.2.1 Linear Gaussian Systems 40 3.2.2 The Kalman Filter Algorithm 43 3.2.3 Illustration 44 3.2.4 Mathemat
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al Derivation of the KF 45 3.3 The Extended Kalman Filter 54 3.3.1 Why Linearize? 54 3.3.2 Linearization Via Taylor Expansion 56 3.3.3 The EKF Algorithm 59 3.3.4 Mathemat
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al Derivation of the EKF 59 3.3.5 Pract
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al Considerations 61 3.4 The Unscented Kalman Filter 65 3.4.1 Linearization Via the Unscented Transform 65 3.4.2 The UKF Algorithm 67 3.5 The Information Filter 71 3.5.1 Canon
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al Parameterization 71 3.5.2 The Information Filter Algorithm 73 3.5.3 Mathemat
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al Derivation of the Information Filter 74 3.5.4 The Extended Information Filter Algorithm 75 3.5.5 Mathemat
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al Derivation of the Extended Information Filter 76 3.5.6 Pract
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al Considerations 77 3.6 Summary 79 3.7 Bibliograph
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al Remarks 81 3.8 Exercises 81 4 Nonparametr
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Filters 85 4.1 The Histogram Filter 86 4.1.1 The Discrete Bayes Filter Algorithm 86 4.1.2 Continuous State 87 4.1.3 Mathemat
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al Derivation of the Histogram Ap
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ximation 89 4.1.4 Decomposition Techniques 92 4.2 Binary Bayes Filters with Stat
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State 94 4.3 The Part
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le Filter 96 4.3.1 Bas
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Algorithm 96 4.3.2 Importance Sampling 100 4.3.3 Mathemat
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al Derivation of the PF 103 4.3.4 Pract
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al Considerations and
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perties of Part
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le Filters 104 4.4 Summary 113 4.5 Bibliograph
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al Remarks 114 4.6 Exercises 115 5 Robot Motion 117 5.1 Introduction 117 5.2 Preliminaries 118 5.2.1 Kinemat
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Configuration 118 5.2.2
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Kinemat
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s 119 5.3 Velocity Motion Model 121 5.3.1 Closed Form Calculation 121 5.3.2 Sampling Algorithm 122 5.3.3 Mathemat
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al Derivation of the Velocity Motion Model 125 5.4 Odometry Motion Model 132 5.4.1 Closed Form Calculation 133 5.4.2 Sampling Algorithm 137 5.4.3 Mathemat
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al Derivation of the Odometry Motion Model 137 5.5 Motion and Maps 140 5.6 Summary 143 5.7 Bibliograph
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al Remarks 145 5.8 Exercises 145 6 Robot Perception 149 6.1 Introduction 149 6.2 Maps 152 6.3 Beam Models of Range Finders 153 6.3.1 The Bas
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Measurement Algorithm 153 6.3.2 Adjusting the Intrins
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Model Parameters 158 6.3.3 Mathemat
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al Derivation of the Beam Model 162 6.3.4 Pract
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al Considerations 167 6.3.5 Limitations of the Beam Model 168 6.4 Likelihood Fields for Range Finders 169 6.4.1 Bas
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Algorithm 169 6.4.2 Extensions 172 6.5 Correlation-Based Measurement Models 174 6.6 Feature-Based Measurement Models 176 6.6.1 Feature Extraction 176 6.6.2 Landmark Measurements 177 6.6.3 Sensor Model with Known Correspondence 178 6.6.4 Sampling Poses 179 6.6.5 Further Considerations 180 6.7 Pract
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al Considerations 182 6.8 Summary 183 6.9 Bibliograph
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al Remarks 184 6.10 Exercises 185 II Localization 189 7 Mobile Robot Localization: Markov and Gaussian 191 7.1 A Taxonomy of Localization
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blems 193 7.2 Markov Localization 197 7.3 Illustration of Markov Localization 200 7.4 EKF Localization 201 7.4.1 Illustration 201 7.4.2 The EKF Localization Algorithm 203 7.4.3 Mathemat
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al Derivation of EKF Localization 205 7.4.4 Phys
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al Implementation 210 7.5 Estimating Correspondences 215 7.5.1 EKF Localization with Unknown Correspondences 215 7.5.2 Mathemat
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al Derivation of the ML Data Association 216 7.6 Multi-Hypothesis Tracking 218 7.7 UKF Localization 220 7.7.1 Mathemat
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al Derivation of UKF Localization 220 7.7.2 Illustration 223 7.8 Pract
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al Considerations 229 7.9 Summary 232 7.10 Bibliograph
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al Remarks 233 7.11 Exercises 234 8 Mobile Robot Localization: Grid And Monte Carlo 237 8.1 Introduction 237 8.2 Grid Localization 238 8.2.1 Bas
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Algorithm 238 8.2.2 Grid Resolutions 239 8.2.3 Computational Considerations 243 8.2.4 Illustration 245 8.3 Monte Carlo Localization 250 8.3.1 Illustration 250 8.3.2 The MCL Algorithm 252 8.3.3 Phys
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al Implementations 253 8.3.4
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perties of MCL 253 8.3.5 Random Part
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le MCL: Recovery from Failures 256 8.3.6 Modifying the
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posal Distribution 261 8.3.7 KLD-Sampling: Adapting the Size of Sample Sets 263 8.4 Localization in Dynam
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Environments 267 8.5 Pract
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al Considerations 273 8.6 Summary 274 8.7 Bibliograph
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al Remarks 275 8.8 Exercises 276 III Mapping 279 9 Occupancy Grid Mapping 281 9.1 Introduction 281 9.2 The Occupancy Grid Mapping Algorithm 284 9.2.1 Multi-Sensor Fusion 293 9.3 Learning Inverse Measurement Models 294 9.3.1 Inverting the Measurement Model 294 9.3.2 Sampling from the Forward Model 295 9.3.3 The Error Function 296 9.3.4 Examples and Further Considerations 298 9.4 Maximum A Posteriori Occupancy Mapping 299 9.4.1 The Case for Maintaining Dependencies 299 9.4.2 Occupancy Grid Mapping with Forward Models 301 9.5 Summary 304 9.6 Bibliograph
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al Remarks 305 9.7 Exercises 307 10 Simultaneous Localization and Mapping 309 10.1 Introduction 309 10.2 SLAM with Extended Kalman Filters 312 10.2.1 Setup and Assumptions 312 10.2.2 SLAM with Known Correspondence 313 10.2.3 Mathemat
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al Derivation of EKF SLAM 317 10.3 EKF SLAM with Unknown Correspondences 323 10.3.1 The General EKF SLAM Algorithm 323 10.3.2 Examples 324 10.3.3 Feature Selection and Map Management 328 10.4 Summary 330 10.5 Bibliograph
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al Remarks 332 10.6 Exercises 334 11 The GraphSLAM Algorithm 337 11.1 Introduction 337 11.2 Intuitive Description 340 11.2.1 Building Up the Graph 340 11.2.2 Inference 343 11.3 The GraphSLAM Algorithm 346 11.4 Mathemat
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al Derivation of GraphSLAM 353 11.4.1 The Full SLAM Posterior 353 11.4.2 The Negative Log Posterior 354 11.4.3 Taylor Expansion 355 11.4.4 Constructing the Information Form 357 11.4.5 Reducing the Information Form 360 11.4.6 Recovering the Path and the Map 361 11.5 Data Association in GraphSLAM 362 11.5.1 The GraphSLAM Algorithm with Unknown Correspondence 363 11.5.2 Mathemat
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al Derivation of the Correspondence Test 366 11.6 Eff
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iency Consideration 368 11.7 Empir
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al Implementation 370 11.8 Alternative Optimization Techniques 376 11.9 Summary 379 11.10 Bibliograph
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al Remarks 381 11.11 Exercises 382 12 The Sparse Extended Information Filter 385 12.1 Introduction 385 12.2 Intuitive Description 388 12.3 The SEIF SLAM Algorithm 391 12.4 Mathemat
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al Derivation of the SEIF 395 12.4.1 Motion Update 395 12.4.2 Measurement Updates 398 12.5 Sparsif
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ation 398 12.5.1 General Idea 398 12.5.2 Sparsif
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ation in SEIFs 400 12.5.3 Mathemat
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al Derivation of the Sparsif
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ation 401 12.6 Amortized Ap
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ximate Map Recovery 402 12.7 How Sparse Should SEIFs Be? 405 12.8 Incremental Data Association 409 12.8.1 Computing Incremental Data Association
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al Considerations 411 12.9 Branch-and-Bound Data Association 415 12.9.1 Recursive Search 416 12.9.2 Computing Arbitrary Data Association
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al Considerations 420 12.11 Multi-Robot SLAM 424 12.11.1 Integrating Maps 424 12.11.2 Mathemat
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al Derivation of Map Integration 427 12.11.3 Establishing Correspondence 429 12.11.4 Example 429 12.12 Summary 432 12.13 Bibliograph
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al Remarks 434 12.14 Exercises 435 13 The FastSLAM Algorithm 437 13.1 The Bas
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Algorithm 439 13.2 Factoring the SLAM Posterior 439 13.2.1 Mathemat
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al Derivation of the Factored SLAM Posterior 442 13.3 FastSLAM with Known Data Association 444 13.4 Im
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ving the
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posal Distribution 451 13.4.1 Extending the Path Posterior by Sampling a New Pose 451 13.4.2 Updating the Observed Feature Estimate 454 13.4.3 Calculating Importance Factors 455 13.5 Unknown Data Association 457 13.6 Map Management 459 13.7 The FastSLAM Algorithms 460 13.8 Eff
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ient Implementation 460 13.9 FastSLAM for Feature-Based Maps 468 13.9.1 Empir
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al Insights 468 13.9.2 Loop Closure 471 13.10 Grid-based FastSLAM 474 13.10.1 The Algorithm 474 13.10.2 Empir
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al Insights 475 13.11 Summary 479 13.12 Bibliograph
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al Remarks 481 13.13 Exercises 482 IV Planning and Control 485 14 Markov Decision
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cesses 487 14.1 Motivation 487 14.2 Uncertainty in Action Selection 490 14.3 Value Iteration 495 14.3.1 Goals and Payoff 495 14.3.2 Finding Optimal Control Pol
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ies for the Fully Observable Case 499 14.3.3 Computing the Value Function 501 14.4 Appl
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ation to Robot Control 503 14.5 Summary 507 14.6 Bibliograph
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al Remarks 509 14.7 Exercises 510 15 Partially Observable Markov Decision
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cesses 513 15.1 Motivation 513 15.2 An Illustrative Example 515 15.2.1 Setup 515 15.2.2 Control Cho
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e 516 15.2.3 Sensing 519 15.2.4 Pred
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tion 523 15.2.5 Deep Horizons and Pruning 526 15.3 The Finite World POMDP Algorithm 527 15.4 Mathemat
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al Derivation of POMDPs 531 15.4.1 Value Iteration in Belief Space 531 15.4.2 Value Function Representation 532 15.4.3 Calculating the Value Function 533 15.5 Pract
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al Considerations 536 15.6 Summary 541 15.7 Bibliograph
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al Remarks 542 15.8 Exercises 544 16 Ap
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ximate POMDP Techniques 547 16.1 Motivation 547 16.2 QMDPs 549 16.3 Augmented Markov Decision
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cesses 550 16.3.1 The Augmented State Space 550 16.3.2 The AMDP Algorithm 551 16.3.3 Mathemat
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al Derivation of AMDPs 553 16.3.4 Appl
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ation to Mobile Robot Navigation 556 16.4 Monte Carlo POMDPs 559 16.4.1 Using Part
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le Sets 559 16.4.2 The MC-POMDP Algorithm 559 16.4.3 Mathemat
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al Derivation of MC-POMDPs 562 16.4.4 Pract
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al Considerations 563 16.5 Summary 565 16.6 Bibliograph
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al Remarks 566 16.7 Exercises 566 17 Exploration 569 17.1 Introduction 569 17.2 Bas
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Exploration Algorithms 571 17.2.1 Information Gain 571 17.2.2 Greedy Techniques 572 17.2.3 Monte Carlo Exploration 573 17.2.4 Multi-Step Techniques 575 17.3 Active Localization 575 17.4 Exploration for Learning Occupancy Grid Maps 580 17.4.1 Computing Information Gain 580 17.4.2
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pagating Gain 585 17.4.3 Extension to Multi-Robot Systems 587 17.5 Exploration for SLAM 593 17.5.1 Entropy Decomposition in SLAM 593 17.5.2 Exploration in FastSLAM 594 17.5.3 Empir
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al Characterization 598 17.6 Summary 600 17.7 Bibliograph
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al Remarks 602 17.8 Exercises 604 Bibliography 607 Index 639
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