Probabilistic robotics:
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cambridge, Massachusetts ; London, England
MIT Press
2006
|
| Schriftenreihe: | Intelligent robotics and autonomous agents
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis Inhaltsverzeichnis Inhaltsverzeichnis |
| Beschreibung: | Hier auch später erschienene, unveränderte Nachdrucke |
| Beschreibung: | XX, 647 Seiten Illustrationen, Diagramme, Karten |
| ISBN: | 9780262201629 0262201623 |
Internformat
MARC
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| 100 | 1 | |a Thrun, Sebastian |d 1967- |e Verfasser |0 (DE-588)112396682 |4 aut | |
| 245 | 1 | 0 | |a Probabilistic robotics |c Sebastian Thrun ; Wolfram Burgard ; Dieter Fox |
| 264 | 1 | |a Cambridge, Massachusetts ; London, England |b MIT Press |c 2006 | |
| 264 | 4 | |c © 2014 | |
| 300 | |a XX, 647 Seiten |b Illustrationen, Diagramme, Karten | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a Intelligent robotics and autonomous agents | |
| 500 | |a Hier auch später erschienene, unveränderte Nachdrucke | ||
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| 700 | 1 | |a Burgard, Wolfram |d 1961- |e Verfasser |0 (DE-588)112720196 |4 aut | |
| 700 | 1 | |a Fox, Dieter |d 1966- |e Verfasser |0 (DE-588)120913933 |4 aut | |
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Datensatz im Suchindex
| DE-BY-862_location | 2000 |
|---|---|
| DE-BY-863_location | 1340 |
| DE-BY-FWS_call_number | 2000/ST 308 T531 1340/ST 308 T531st |
| DE-BY-FWS_katkey | 421016 |
| DE-BY-FWS_media_number | 083000522359 083101177481 |
| _version_ | 1824996696089690113 |
| adam_text |
Brief
Contents
1 Basics
1
2
Introduction
3
2
Recursive State Estimation
13
3
Gaussian Filters
39
4
Nonparametric Filters
85
5
Robot Motion
117
6
Kobor Perception
149
II Localization
189
7
Mobile Robot Localization: Markov and Gaussian
191
S
Mobile Robot Localization: Grid And Monte Carlo
237
III Mapping
279
9
Occupancy Grid Mapping
281
10
Simultaneous Localization and Mapping
309
11
The GraphSLAM Algorithm
337
22
The Sparse Extended Information Filter
385
23
The FastSLAM Algorithm
437
IV Planning and Control
485
14
Markov Decision Processes
487
15
Partially Observable Markov Decision Processes
513
v{ Brief Contents
16
Approximate POMDP Techniques
547
17
Exploration
569
Contents
Preface xvii
Acknowledgments xix
I Basics 1
1 Introduction 3
1.1 Uncertainty in Robotics 3
1.2 Probabilistic Robotics 4
1.3 Implications 9
1.4 Road Map 10
1.5 Teaching Probabilistic Robotics 11
1.6 Bibliographical Remarks 11
2 Recursive State Estimation 13
2.1 Introduction 13
2.2 Basic Concepts in Probability 14
2.3 Robot Environment Interaction 19
2.3.1 State 20
2.3.2 Environment Interaction 22
2.3.3 Probabilistic 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 Mathematical Derivation of the Bayes Filter 31
2.4.4 The Markov Assumption 33
viii Contents
2.5 Representation and Computation 34
2.6 Summary 35
2.7 Bibliographical 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 Mathematical 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 Mathematical Derivation of the EKF 59
3.3.5 Practical 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 Canonical Parameterization 71
3.5.2 The Information Filter Algorithm 73
3.5.3 Mathematical Derivation of the Information Filter 74
3.5.4 The Extended Information Filter Algorithm 75
3.5.5 Mathematical Derivation of the Extended
Information Filter 76
3.5.6 Practical Considerations 77
3.6 Summary 79
3.7 Bibliographical Remarks 81
3.8 Exercises 81
4 Nonpar atnetric 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 Mathematical Derivation of the Histogram
Approximation 89
Contents ix
4.1.4 Decomposition Techniques 92
4.2 Binary Bayes Filters with Static State 94
4.3 The Particle Filter 96
4.3.1 Basic Algorithm 96
4.3.2 Importance Sampling 100
4.3.3 Mathematical Derivation of the PF 103
4.3.4 Practical Considerations and Properties of Particle
Filters 104
4.4 Summary 113
4.5 Bibliographical Remarks 114
4.6 Exercises 115
5 Robot Motion 117
5.1 Introduction 117
5.2 Preliminaries 118
5.2.1 Kinematic Configuration 118
5.2.2 Probabilistic Kinematics 119
5.3 Velocity Motion Model 121
5.3.1 Closed Form Calculation 121
5.3.2 Sampling Algorithm 122
5.3.3 Mathematical 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 Mathematical Derivation of the Odometry Motion
Model 137
5.5 Motion and Maps 140
5.6 Summary 143
5.7 Bibliographical 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 Basic Measurement Algorithm 153
6.3.2 Adjusting the Intrinsic Model Parameters 158
6.3.3 Mathematical Derivation of the Beam Model 162
x Contents
6.3 A Practical Considerations 167
6.3.5 Limitations of the Beam Model 168
6.4 Likelihood Fields for Range Finders 169
6.4.1 Basic 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 Practical Considerations 182
6.8 Summary 183
6.9 Bibliographical Remarks 184
6.10 Exercises 185
II Localization 189
7 Mobile Robot Localization: Markov and Gaussian 191
7.1 A Taxonomy of Localization Problems 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 Mathematical Derivation of EKF Localization 205
7.4.4 Physical Implementation 210
7.5 Estimating Correspondences 215
7.5.1 EKF Localization with Unknown
Correspondences 215
7.5.2 Mathematical Derivation of the ML Data
Association 216
7.6 Multi Hypothesis Tracking 218
7.7 UKF Localization 220
7.7.1 Mathematical Derivation of UKF Localization 220
7.7.2 Illustration 223
7.8 Practical Considerations 229
Contents xi
7.9 Summary 232
7.10 Bibliographical 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 Basic 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 Physical Implementations 253
8.3.4 Properties of MCL 253
8.3.5 Random Particle MCL: Recovery from Failures 256
8.3.6 Modifying the Proposal Distribution 261
8.3.7 KLD Sampling: Adapting the Size of Sample Sets 263
8.4 Localization in Dynamic Environments 267
8.5 Practical Considerations 273
8.6 Summary 274
8.7 Bibliographical 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
xii Contents
9.4.2 Occupancy Grid Mapping with Forward Models 301
9.5 Summary 304
9.6 Bibliographical 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 Mathematical 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 Bibliographical 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 Mathematical 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 Mathematical Derivation of the Correspondence
Test 366
11.6 Efficiency Consideration 368
11.7 Empirical Implementation 370
Contents xiii
11.8 Alternative Optimization Techniques 376
11.9 Summary 379
11.10 Bibliographical 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 Mathematical Derivation of the SEIF 395
12.4.1 Motion Update 395
12.4.2 Measurement Updates 398
12.5 Sparsification 398
12.5.1 General Idea 398
12.5.2 Sparsification in SEIFs 400
12.5.3 Mathematical Derivation of the Sparsification 401
12.6 Amortized Approximate Map Recovery 402
12.7 How Sparse Should SEIFs Be? 405
12.8 Incremental Data Association 409
12.8.1 Computing Incremental Data Association
Probabilities 409
12.8.2 Practical Considerations 411
12.9 Branch and Bound Data Association 415
12.9.1 Recursive Search 416
12.9.2 Computing Arbitrary Data Association
Probabilities 416
12.9.3 Equivalence Constraints 419
12.10 Practical Considerations 420
12.11 Multi Robot SLAM 424
12.11.1 Integrating Maps 424
12.11.2 Mathematical Derivation of Map Integration 427
12.11.3 Establishing Correspondence 429
12.11.4 Example 429
12.12 Summary 432
12.13 Bibliographical Remarks 434
12.14 Exercises 435
13 The FastSLAM Algorithm 437
13.1 The Basic Algorithm 439
xiv Contents
13.2 Factoring the SLAM Posterior 439
13.2.1 Mathematical Derivation of the Factored SLAM
Posterior 442
13.3 FastSLAM with Known Data Association 444
13.4 Improving the Proposal 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 Efficient Implementation 460
13.9 FastSLAM for Feature Based Maps 468
13.9.1 Empirical Insights 468
13.9.2 Loop Closure 471
13.10 Grid based FastSLAM 474
13.10.1 The Algorithm 474
13.10.2 Empirical Insights 475
13.11 Summary 479
13.12 Bibliographical Remarks 481
13.13 Exercises 482
IV Planning and Control 485
14 Markov Decision Processes 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 Policies for the Fully
Observable Case 499
14.3.3 Computing the Value Function 501
14.4 Application to Robot Control 503
14.5 Summary 507
14.6 Bibliographical Remarks 509
14.7 Exercises 510
Contents xv
15 Partially Observable Markov Decision Processes 513
15.1 Motivation 513
15.2 An Illustrative Example 515
15.2.1 Setup 515
15.2.2 Control Choice 516
15.2.3 Sensing 519
15.2.4 Prediction 523
15.2.5 Deep Horizons and Pruning 526
15.3 The Finite World POMDP Algorithm 527
15.4 Mathematical 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 Practical Considerations 536
15.6 Summary 541
15.7 Bibliographical Remarks 542
15.8 Exercises 544
16 Approximate POMDP Techniques 547
16.1 Motivation 547
16.2 QMDPs 549
16.3 Augmented Markov Decision Processes 550
16.3.1 The Augmented State Space 550
16.3.2 The AMDP Algorithm 551
16.3.3 Mathematical Derivation of AMDPs 553
16.3.4 Application to Mobile Robot Navigation 556
16.4 Monte Carlo POMDPs 559
16.4.1 Using Particle Sets 559
16.4.2 The MC POMDP Algorithm 559
16.4.3 Mathematical Derivation of MC POMDPs 562
16.4.4 Practical Considerations 563
16.5 Summary 565
16.6 Bibliographical Remarks 566
16.7 Exercises 566
17 Exploration 569
17.1 Introduction 569
17.2 Basic Exploration Algorithms 571
17.2.1 Information Gain 571
xvi Contents
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 Propagating 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 Empirical Characterization 598
17.6 Summary 600
17.7 Bibliographical Remarks 602
17.8 Exercises 604
Bibliography 607
Index 639
Brief
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Contents
Preface
xvii
xix
1 Basics 1
1
Introduction
З
1.1
Uncertainty
¡η
1.2
l-Ynbabilistit Robotics
4
1.3
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211
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1
lni
md
uč
tion
11"
^
2
['reliiiiniarifb
llfî
\2
1
K
menu tic
C
on figura lion lití
І.2 2
Pnibdbilistk Kiiieiiuiifs
11*?
5.3
Velocily Moinm
Міч
-Jel
Ł21
5-3
I Closed horrn Cikuidlum
121
5 3.2
Sampling
.\l^<ir¡Ilim
122
5.3.3
Mathematica]
Doi
ιν,ιΗοη
ot
trii' Vtjlntit\
Motion
M
od
ol
125
τ
4
Od
umet
r v
Motion
Model
132
■>.4
1
Closed Form Calculation
133
[■4 2
S.nmplinj'
Al^ìnthm
137
5 43
\I,ithi'm,ìli(.ìl
ľJenv^tion
of the Odometty Motion
Model
147
5.5
Motion and
M.ip^ 14li
5.6
Summary
14^
5.7
Biblio^rLiphica]
Remai
кь
345
145
6
iitiboi Perception
14У
ή
I Intruduttwn
1 №
fi
2
Млрч
ľí2
ћ
?'
fíťiim Mtidťls
of kanjţe
E iiider1· 151i
6 ?.
І І
hi.'
finsk'
Mľ.isurenient
Aljţiinthm L53
6 3 2
Adjusting
thľ
IntrmsK Model 1'drdmeters 15b
6.3.3
Mathoma
tical
Поті
гм
h
on tit the fJfjni Model
162
β Ί.4
Practicai
Con si derations
167
6 3.5
Limitations
ol
the Beam
Model
L
ЬН
6.4
Likelihood Fields for
Rúti
go
Hnders
169
6.4.1
Hasú
Algorithm
1
6У
6.4.2
Fx tensions
172
6.1!
Correla
(ion-Rised Measurement Models
174
6-6
Feature-Based
M l'apurement
Models
17<>
6-6 1
Feature
Ľxlraction
17ñ
6-0.2 [
jTi
dma
ґк
Measurements
177
fr.6.3 Sensor Model with Known Correspondence
178
ñ.íi
Λ
Samp\m% Poses
179
Ŕ.ťi
5
Further
ConsidťrjlH'ľis
ISO
ti.7 Prat titdl Considerations
1Й2
6.& Summdry IS3
6Я
Bibliographical Remarks
184
6.10
Exercise
ІЙ5
IT
Lútľalization T
89
Locali
zu!
ion:
Markov and
Gaussî û
ti
191
7.1
A
Taxonomy of
Loca
І і
za
(ion Problems
193
7.2
Markov
І.пнгаІі-^Іюп
197
7.3
Illusii-aiion ofMarko\ Locíilizalion
200
7.4
hKP
Localization
201
7.4 1
Illustration
201
7.4 2
"Піє
EKF
Localisation Algorithm
203
7.4.3
Mathemdtkđl
Derivation of
hKť
Localization
20^
7.4.4
Physical I rap
lenienia
t
ion
21(1
7
^ Estimating Correspondences
213
7.'i
1
EKF
Localiřatinn
with Unknown
Cot respond
en ce1!
215
7^2
Ma (hem ,i
tirül
Deri\ dtion of the ML Udtd
Association
2
Hi
7.6
Multi-I lypolhe&is Tracking
218
7.7
UKh I.wdlization
220
7.7Л
Malhenidtical Derivation or LK1: Localization
220
7 7.2
Illustration
223
7.8
ľríiťlical
ConsidcraEums 224?
Conicii!--
7 9
Summary
232
7
IO lìibliogmphioiL
Remarks
233
7.Г1
Exercises
234
Mobile Robot Lofiilization. Grid
Aud
Monte
Cario
237
8 1
Introduction
237
M-2 Grid
Locíi
Liza
tion
238
8.2.1
Basic Algorithm 23&
8.2.2
Grid Resolutions
234
8
2.Л
Computai
muai
Considerations
243
Л
2.4
]llu4lr.ìluin
24.^
8 3
Mon
U:
Crii lo
Localization
250
S3 1
Illustration
230
8.Л
2
Tlie
MCL Algorithm
2ï2
8.3
"ΐ
!łhjsu'jl I
mp
Iŕm
і'п
Iuti
un s
ľi.i
S.3 4
[Vopcriics
oí
MCL
253
3.3.5
Randám
Particle MCL. Recovery from
Fdilurť^
2^6
M.
3.6
.Modifying the
Jłropoh*łt
Distribution Zl>l
H
'M
KLD-Sdinplmj^:
Ad^pling
lhe
Si/e <it
Sample
Ы'Ль
263
H.4
Lofjli/.űtion in
ЈЗулдгшс
Hnvironmciiti?
26"
tí
5
ľiactical
Considerations
27Ъ
3.6
Summary
274
8.7
Bibliographical ReiiMrLs
275
8
H hxernsf's
27<i
III Mapping
279
Mapping
281
4.1
Introduction
281
Ч
2
I he
Otťupjnry Cind
Mapping Alj^iintbm
284
4.2.1
M
ult
і
-Sensor Fusion
293
4.3
Learning Inverse Measurement Models
294
'Λ3
1
Inverting the Meusuri'mi'Dl Modi'J
244
4.3 2
S-imphn^
írom
the- Korward Model
295
93 3
The Frror Function
2%
9.3.4
Examples and Furlher Considerations
9.4
Maximum
A Postenou
Occupancy Mapping
У
4 1
I he Cjse fur Maintaining l^'pi'niJennes
XII
4 4 2
(.'сеч
poney
í.ii id M.ippmg ivLfh
Forward Models
30
Я
5
Summary
304
Я.в
Bibliograph]
cal
Remarks
305
4.7
Extfd^
307
10
Siiftuìtnticous
Localization ami
Maf/ping
ЗАЧ
Ltì.l
lntrodnctinn
304
IQ
2
SLAM with Emended
Kalman
Filíc-is
312
1(1.2.1
Sel
и
ρ ,ι η
d
Assumptions
ül
2
1(1 2.2
SLAM wilts
Китч
n Cttrrťspimdťnrť
.-îl
'■
](] 2.3
M.itrn'm.itujl
L^nvatmn uf hKl·
Ы
AM
417
10 3
hKFSl
AM with Unknown Co
rrc
sponden
α.1*
323
10 3 1
rht'LioriLriil
FKK
bl
AM Alpontlim 323
10.3.2
Ехлтріеч
324
IÛ.3.3
Fra
I u ľŕ
Sciceli
o n and
Мдр
Management 32i
10.4
S u
mnu
Γ)
_ł_łlł
10.5
Bibluïjçraplutjl
Remarks
Ì.12
334
11
The GrnphbLAM Algorithm
337
11.1
JntRiduLtioTi
ЗЛ7
11.2
Intinti ve
Desť-nptLim
340
11 2.1
ISiiiidintf
Lptliľ(,ľ,iph
340
11 2.2
Jnfcrcnt-c
Я4^
I І
3
Tlu'CiHiphSI AM Algoiitbin
34ο
11.4
Ma
Üiema
li ca]
Derivation of
Gra
phS LA
M
353
11.4.1
The Full
SLAM
1\>ч
tenor JSJ
11.4 2
[he
Ni^.itHi'
I oj; l'tistcrun
ЗЇ4
Γ
1.4 1
Liylor F\p.iiisi(>n
^^
1
1.4 4
Cansłr^ctmg
tlu.' I n to
гт
.iti
ο η Κοητι
357
І
1-4.5
Kod
lining
tlu·
]
η Ιοί
in a I ion Form 36U
I I
4 !■>
Recovťťmfc
the T.ith and the Map
361
1
1-5
Data Association in UraphSLAM
362
11.5.1
The GraphSLAM Al^onthni
w
úh
Unknown
Correspondence 3(i3
11.
Ч.2
\l
л
tlie
піл
Cil
л
1
De
π
v
ľi
ti
ш
ι
nf tlie
CorresptniJeiitt1
Test \bb
II
ŕ>
Kftif](łnt
y t.ťsnsidiT.jtum
368
1 1 7
l· m p L n
с л
1
Inipľľiiiľiif.itiiiii
370
] 1
H
AherridtL' e
Opt
ι
inizii
ion Techniques 37b
11 9
Sumnurv
Ì7<J
]]
Ш
lÌLbhiì^r.iphitdl Keindrks
ЛН1
11
І І Ь'хсгсіч'ч
Ì82
12
Tlir Sparge
Extended
in
f
ormat ioti
hütet 385
12.1
Introdurrmi!
ΙΗί
[S
2
Intuitive
[ÏL'soiplion
ISft
12.3
The
SEIF SLAM
Alfiorilhm
391
12.4
Miithomo
honi
Ιλτιν,ιΐιοη
of EhobKIl·
ЧУЅ
12.4.1
Моііші
Updaio
395
12.4.2
Mť^uiĽiiiĽni
Updatet 39S
12
ť Spdr^LŕiÍLitJun
ј4,Ч
12^
J CiTi.T.il IJim
№
ПЯ
2
Sp.-iisLÍir.itiim m Shll·^
4(1(1
32.53
Mathematical
JX'riv.iIion
л
ŕ thi.'Sp,iľsifir,iti[in 4IH
12.5
Amortized
Appmxiinalc
Map Recovery
-Ш2
12 7
Huw
Sparse ShirnUI SELFs Be?
4Ü5
12
H
Incremental
Dati» Associ
¿»(ion
40^
12.8 1
Cnnipuling l[n.renieiii,il Data Associcilion
PmbđbilitLtiS
409
12.8
Z ľr,jLtkj]
QmsidtirJiLoib
411
І2.Ч
|tmneh-rTnd-f&£)Ljrn.J
Ії.ііл
Λ
sst jí
i j
tion
41т
12.
Ч І
iíecurMveS.Mrch 4lf»
12.9 2
Computing Arbitrary Data
Associo
t
ion
Probabilities»
416
12.
'í J
El|ul\
a lence
Constrímits
419
12.10
ľrui
tuai
Considera
tians 42U
12 11 MuJti-Kübot SLAM 4Z4
12 11.1
Intťgratmg M.ips
424
12
I 1
.2
MíHiemíitiCíil
І Хтнчттюп
tjf \1^p Inlejtr^tiím
427
12.11.3
ĽslablĹuhing
С
ci
r
respon
dc na.·
429
1211.4
example
429
12 12
Summary
412
12
ІЗ
Uiblto^ľ.iphiail
Кутплгкѕ
4.14
12 14
FxLTk-i^s
13
The FíistSLAM
Algorithm
437
ì*
1
"Hit Bd^Lt.
Alüoriihm
4
У)
X¡V
13.2
Factoring
lhe
SLAM Posterior
13.2Л
Mathematical Derivation of
lhe
ł-dLtored
SLAM
Posterior
442
13.3 histSLAM
with Known Datd
Association
444
13 4
Impubi
n g
th
ť
Proposal Distribution
451
13 4.1
Extending
lhe
Path Posterior by Sampling a Ncvv
Ним
451
13 4.2
Updating the Observed Feature
Eslimale
41^4
13 4.3
С.Л
Ic-uldting importance Factors
455
13 5
Unknown Data Assoridtion
457
13 6
Мдр
Mjnđ
gement
45^
13 7
The PaslSLAM Algorithms 46U
13
H Efŕicitnl
Implementa
lion
460
13 9
FdstSLAM for Feature-Based Maps 46rt
139.1
Empirical InsiRhts
468
13.9.2
Loop Closure
471
13 10
Grid-based FaslSLAM
474
13.10.1
Піе
Algorithm
474
13.10.2
Empirical Insights
475
13.11
Summary
479
13.12
Bibliographical Remarks
481
13.13
Exercises
482
IV Planning and Control
485
14
Marknv Decision Pracesseb
487
14.1
Motivation
487
14.2
Uncertainty in Action Selection
14.3
Value Iteration
495
14.3.1
Goals
,
-indP.iyoíi
4Ч5
14.3.2
Finding Optima] Control Polirii's for the Fully
143-3
Computing the \'j]ue Function
501
14.4
Application to Robot Control
503
14.
S
Siimm^iy
477
14.6
Bibliogr.iphic-il
Rťnurks
5l>J
14.7
15
Půrtiaily
Observable
Markov Decision Processes
513
1Ť
I Motivation
"ΐΠ
15 2
Λη
]
Ι
lustra ti ve hxdmpJe
тГт
15.2 1
Setup
^ľi
15.2.2
Control Choice 5\fi
15-2.3
Sensing
õl1'
15.2.4
Prediction
523
14 2 4
Deep Horizons and Pruning
526
15.Л
lhe
Tinite World POMDP Algorithm
527
15.4
Mathematical Derivation
oí
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
ľi^cÍicil
Considerations
4Ή>
156
ííiimmarv
541
15.7
Bibi
ι
ogra
рћ
¡Ctil
Reni-irk^
542
15.8
Exercises
544
16
Approximate POMDP Techniques
547
\h I Motivation
547
Ita.
2
QMDPs
544
16 3
Augmented Markov
Deci
sum Prt Hisses
16.3.1
The Augmented btjtc
Space
16 3.2
The AMDP Algorithm
551
lf>
3 1
Mdthtímatical
Derivation of AMDPs
553
16 3 4
Application
to Mobile Robot Navigation
556
16.4
MtmteCdrltjPOMDPb 551*
lii
4.1
Uhing Particle Sets
559
№ 4.2
l"he NfC-POMDP Algorithm
559
Hi
4.3
Vlathfrnuititdl Derivation af MC-POMDPs
562
lfi
4.4
]јгјг1кј1
Considerations
165
Summaiy
56^
là.
6
Bibliographical
Кетлгкь
5bb
Hi.
7
Е че
reiser
566
17
fïjj/ürdrjüfl
569
17.1
Introduction
569
17.2
Basic Explosion Algoiithm^,
571
17.2.1
Inforni a
(ion Gam
571
OJfJf'N-
172.2
Greedy Techniques
572
172 3
Montr Carlo Exploration
573
172.4
M
ulii
-Sic-
p
Techniques
575
173
Active I .ocdili/olion
573
I
7 4
FxploiMdon
loi
I earning
Осоірлік
\
Cind
Млрч
174
J t
(nnpuiinjţ
liitiirni.itJiiTi
Culi
π
'ïMll
]7.4
2
Propnuli
t
ιη^
C
ni in Wi
17.4 1
hxtľnsuin
to Multi-Kiibol Systfin1- ^
17
^ t-xploration
rar
SI. AM
WÌ
17.
τ
1
E-ntrí)p\
IJi'(
timposituìii in Si
Λ
M
^9
17.
τ
2
Exploration
m
E^istSLAM
^91
17.^
"ì
ť
m
pi
n
ci
I Ch.jr.K tiTi/titiim
^'Ж
I7.<i Siinimar)
ťiíJÍJ
17.7
Biblu^rLiphiLcj]
Rdiiurks tiO2
178
Exercises
ЫЧ
Index
Ь39 |
| adam_txt |
Brief
Contents
1 Basics
1
2
Introduction
3
2
Recursive State Estimation
13
3
Gaussian Filters
39
4
Nonparametric Filters
85
5
Robot Motion
117
6
Kobor Perception
149
II Localization
189
7
Mobile Robot Localization: Markov and Gaussian
191
S
Mobile Robot Localization: Grid And Monte Carlo
237
III Mapping
279
9
Occupancy Grid Mapping
281
10
Simultaneous Localization and Mapping
309
11
The GraphSLAM Algorithm
337
22
The Sparse Extended Information Filter
385
23
The FastSLAM Algorithm
437
IV Planning and Control
485
14
Markov Decision Processes
487
15
Partially Observable Markov Decision Processes
513
v{ Brief Contents
16
Approximate POMDP Techniques
547
17
Exploration
569
Contents
Preface xvii
Acknowledgments xix
I Basics 1
1 Introduction 3
1.1 Uncertainty in Robotics 3
1.2 Probabilistic Robotics 4
1.3 Implications 9
1.4 Road Map 10
1.5 Teaching Probabilistic Robotics 11
1.6 Bibliographical Remarks 11
2 Recursive State Estimation 13
2.1 Introduction 13
2.2 Basic Concepts in Probability 14
2.3 Robot Environment Interaction 19
2.3.1 State 20
2.3.2 Environment Interaction 22
2.3.3 Probabilistic 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 Mathematical Derivation of the Bayes Filter 31
2.4.4 The Markov Assumption 33
viii Contents
2.5 Representation and Computation 34
2.6 Summary 35
2.7 Bibliographical 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 Mathematical 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 Mathematical Derivation of the EKF 59
3.3.5 Practical 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 Canonical Parameterization 71
3.5.2 The Information Filter Algorithm 73
3.5.3 Mathematical Derivation of the Information Filter 74
3.5.4 The Extended Information Filter Algorithm 75
3.5.5 Mathematical Derivation of the Extended
Information Filter 76
3.5.6 Practical Considerations 77
3.6 Summary 79
3.7 Bibliographical Remarks 81
3.8 Exercises 81
4 Nonpar atnetric 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 Mathematical Derivation of the Histogram
Approximation 89
Contents ix
4.1.4 Decomposition Techniques 92
4.2 Binary Bayes Filters with Static State 94
4.3 The Particle Filter 96
4.3.1 Basic Algorithm 96
4.3.2 Importance Sampling 100
4.3.3 Mathematical Derivation of the PF 103
4.3.4 Practical Considerations and Properties of Particle
Filters 104
4.4 Summary 113
4.5 Bibliographical Remarks 114
4.6 Exercises 115
5 Robot Motion 117
5.1 Introduction 117
5.2 Preliminaries 118
5.2.1 Kinematic Configuration 118
5.2.2 Probabilistic Kinematics 119
5.3 Velocity Motion Model 121
5.3.1 Closed Form Calculation 121
5.3.2 Sampling Algorithm 122
5.3.3 Mathematical 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 Mathematical Derivation of the Odometry Motion
Model 137
5.5 Motion and Maps 140
5.6 Summary 143
5.7 Bibliographical 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 Basic Measurement Algorithm 153
6.3.2 Adjusting the Intrinsic Model Parameters 158
6.3.3 Mathematical Derivation of the Beam Model 162
x Contents
6.3 A Practical Considerations 167
6.3.5 Limitations of the Beam Model 168
6.4 Likelihood Fields for Range Finders 169
6.4.1 Basic 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 Practical Considerations 182
6.8 Summary 183
6.9 Bibliographical Remarks 184
6.10 Exercises 185
II Localization 189
7 Mobile Robot Localization: Markov and Gaussian 191
7.1 A Taxonomy of Localization Problems 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 Mathematical Derivation of EKF Localization 205
7.4.4 Physical Implementation 210
7.5 Estimating Correspondences 215
7.5.1 EKF Localization with Unknown
Correspondences 215
7.5.2 Mathematical Derivation of the ML Data
Association 216
7.6 Multi Hypothesis Tracking 218
7.7 UKF Localization 220
7.7.1 Mathematical Derivation of UKF Localization 220
7.7.2 Illustration 223
7.8 Practical Considerations 229
Contents xi
7.9 Summary 232
7.10 Bibliographical 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 Basic 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 Physical Implementations 253
8.3.4 Properties of MCL 253
8.3.5 Random Particle MCL: Recovery from Failures 256
8.3.6 Modifying the Proposal Distribution 261
8.3.7 KLD Sampling: Adapting the Size of Sample Sets 263
8.4 Localization in Dynamic Environments 267
8.5 Practical Considerations 273
8.6 Summary 274
8.7 Bibliographical 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
xii Contents
9.4.2 Occupancy Grid Mapping with Forward Models 301
9.5 Summary 304
9.6 Bibliographical 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 Mathematical 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 Bibliographical 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 Mathematical 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 Mathematical Derivation of the Correspondence
Test 366
11.6 Efficiency Consideration 368
11.7 Empirical Implementation 370
Contents xiii
11.8 Alternative Optimization Techniques 376
11.9 Summary 379
11.10 Bibliographical 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 Mathematical Derivation of the SEIF 395
12.4.1 Motion Update 395
12.4.2 Measurement Updates 398
12.5 Sparsification 398
12.5.1 General Idea 398
12.5.2 Sparsification in SEIFs 400
12.5.3 Mathematical Derivation of the Sparsification 401
12.6 Amortized Approximate Map Recovery 402
12.7 How Sparse Should SEIFs Be? 405
12.8 Incremental Data Association 409
12.8.1 Computing Incremental Data Association
Probabilities 409
12.8.2 Practical Considerations 411
12.9 Branch and Bound Data Association 415
12.9.1 Recursive Search 416
12.9.2 Computing Arbitrary Data Association
Probabilities 416
12.9.3 Equivalence Constraints 419
12.10 Practical Considerations 420
12.11 Multi Robot SLAM 424
12.11.1 Integrating Maps 424
12.11.2 Mathematical Derivation of Map Integration 427
12.11.3 Establishing Correspondence 429
12.11.4 Example 429
12.12 Summary 432
12.13 Bibliographical Remarks 434
12.14 Exercises 435
13 The FastSLAM Algorithm 437
13.1 The Basic Algorithm 439
xiv Contents
13.2 Factoring the SLAM Posterior 439
13.2.1 Mathematical Derivation of the Factored SLAM
Posterior 442
13.3 FastSLAM with Known Data Association 444
13.4 Improving the Proposal 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 Efficient Implementation 460
13.9 FastSLAM for Feature Based Maps 468
13.9.1 Empirical Insights 468
13.9.2 Loop Closure 471
13.10 Grid based FastSLAM 474
13.10.1 The Algorithm 474
13.10.2 Empirical Insights 475
13.11 Summary 479
13.12 Bibliographical Remarks 481
13.13 Exercises 482
IV Planning and Control 485
14 Markov Decision Processes 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 Policies for the Fully
Observable Case 499
14.3.3 Computing the Value Function 501
14.4 Application to Robot Control 503
14.5 Summary 507
14.6 Bibliographical Remarks 509
14.7 Exercises 510
Contents xv
15 Partially Observable Markov Decision Processes 513
15.1 Motivation 513
15.2 An Illustrative Example 515
15.2.1 Setup 515
15.2.2 Control Choice 516
15.2.3 Sensing 519
15.2.4 Prediction 523
15.2.5 Deep Horizons and Pruning 526
15.3 The Finite World POMDP Algorithm 527
15.4 Mathematical 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 Practical Considerations 536
15.6 Summary 541
15.7 Bibliographical Remarks 542
15.8 Exercises 544
16 Approximate POMDP Techniques 547
16.1 Motivation 547
16.2 QMDPs 549
16.3 Augmented Markov Decision Processes 550
16.3.1 The Augmented State Space 550
16.3.2 The AMDP Algorithm 551
16.3.3 Mathematical Derivation of AMDPs 553
16.3.4 Application to Mobile Robot Navigation 556
16.4 Monte Carlo POMDPs 559
16.4.1 Using Particle Sets 559
16.4.2 The MC POMDP Algorithm 559
16.4.3 Mathematical Derivation of MC POMDPs 562
16.4.4 Practical Considerations 563
16.5 Summary 565
16.6 Bibliographical Remarks 566
16.7 Exercises 566
17 Exploration 569
17.1 Introduction 569
17.2 Basic Exploration Algorithms 571
17.2.1 Information Gain 571
xvi Contents
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 Propagating 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 Empirical Characterization 598
17.6 Summary 600
17.7 Bibliographical Remarks 602
17.8 Exercises 604
Bibliography 607
Index 639
Brief
Contents
I Basics
1
1
¡ч!
roditi
I
io·!
3
2
Ri'cui'iii'i' Sli'fc
Еъ1
німіціч І
?-
'>
Gauguin Fiih-r·:
34
4
Nimfiiiiitiiii'tric
FditV^
#5
Ї
Robot Molavi
117
її
Rabot
Peaą-lioa
144
И
Localization
189
7
Mobili-
Rolwt
Ι γί
¡dizuHon
Marhoz-
¡¡mí Gíi\naiaii
191
ť
Mobile Roil'!
І осліі'яііон
Cr
id
Aud
Monte
Curio ZÜ7
III Mapping
279
^1
Ос-.щііічсіі
Grid
¿\Αίίρρϊιι$
281
f'J
-ìiniititiiricflit*
Locuit
¿я
t
¡ot
ι
ши
Mtipym^
ίί)4
f ľ ŕ hť Gmpii^LAM Ah-onlliiti
3-3?
i
2 1
liľ
^јч/!-.-
Lxiťtidťii
infarinai
toii
Filler
3¡35
13
thvla^blAMAt^nifaii
437
IV Planning and Control 4S5
14
Miirfco:-
Den-non
Piot fws
487
V5 Pai'tiiiïly
Ol^crvjbU' Mackor
Ιλ-η-.ιοιι
l'uw^c-
'^\^
Contents
Preface
xvii
xix
1 Basics 1
1
Introduction
З
1.1
Uncertainty
¡η
1.2
l-Ynbabilistit Robotics
4
1.3
implications
У
1.4
Kt
jad
Map
10
1.5
h^rbing Probabilistic Robotics
11
1.6
ІііЬіш^гзріііі-лІ
Rťitirtrkti
11
fitÍumivť
State Estimation
13
2 1
Intnxhiction
11
2.2
lì
j
si
г С
Amt
ер
Es in l'robdbiht) 14
2 3 Robot hiwiroiiintiiit
ііПєґдсїіоіі
19
2 1.1 State
2tì
2 3.2
Rnvircmment Inttraciiun
22
211
Probabilistic
Genera
liv c
LíWb
24
2 3 4
iHiťf
Distributions
25
2 4
Eia>ťs
biters
2ñ
2.4
I I he
ЕЅлуеѕ
Filter
Alfturilhiii
2fŕ
2.4 2
U
sample
2S
2.4.3
Mathematical Derivation of
th.·
EJayes
H t
ter
31
2.-4 4
Tlie Markov
Assumption
33
2.1
Re presen
Ui tui
n
and Compute
tum
34
Lb
Sum
ui
Liry
35
2.7
liibhogrjphitci]
Remarks
36
2-8
Fxerust1^
3h
HíYťrs
34
ł.l I
n
tradii
ci
ι
o
n
39
3.2 1
lu·
Κ.ι
]
пил Г
ι
Ilei
40
? 2
I I mra
r G
judian Svbíen-H
40
3.2.2
ТЋс
K.ilman
Filter
Αΐ^ΟϊϊΙηηι Ή
123
llhiMiotinn
44
\2 4
MèithtìmaiLcal IViiv.n
turn trf the
KF
45
31
I
he lìx
tended
ΚαΙιη.ιη
hilt.T
^i
ЯЛ І
Why
Linearizo?
54
Ъ-3
2 [
і
π
ел
π
¿Ji
ι
on
\гід
Taylor b\p,jnsiün ^6
3.3 3
Ibi·
E-KI-AlHiinthm
^)
Χ? Λ
M.nthťm.itii-.i]
Dérivation
of
ItiĽ F-K h
^9
"i.^ "ï
Pľ.iclk'.il ť.
niisnaiTtitiiins
61
34
Ihľ L.iisiťľiied
Kalman
Filter
ѓИ
3-4
I
[
jne.inzaiion Vi,i I he Unsrented
IrLiJisfoini
65
342
¡he UKh A[^>nthm
^7
ЗЛ
Thf Intorm^titm l-ilier
71
3.5.1
Глпопк-лІ
['a
Mínete
π
Zel
I ion
71
3 5.2
Tilo
In form ,i
tion
Γι
Itt
г
Algori
I h m
73
1
"ί
3
Mflthom¿itiLMl
1λ·η\
jlKíiiiit
the
Informo
linn Filter
3 5 4
The Cxtendrd Inloi motion filter Algorithm
75
3 5 5
Mathematical Derivation or the I
xtenJud
InľiirniatiQľi
rilter 7b
3.5.Ѓ·
Practual Cnnsiderations
77
Л
b Summaiy
74
1 7
Bibli
ii^ra
pli
ісл
I Remarks HI
3 8
LxerciSL-s
Λ
I
4
Noitparamctnr Filters
85
4
I TheHblogi-im hilh-r
№
4.1.1
The
lástrete
bd\es Filter Al^ontlim Sfi
4 1.2
ConlJmKHis St.iH·
87
4 1.3
Mathematical [.X'nviitiim tif
tłie
Appi'OX
im,
il
юн
S9
l'i
4.14
Docom
position
1<·ι
(iniques Lï2
4 2
Binary
Ва*еь
Filters
wüh
SM
tic
Ы
-iti1
'П
4 3
The Particle Filler 1'6
41.1
Basic Algorithm
%
4
\2
Importante Sampling
]00
4 3.3
NLnthemdtkdJ Demrtlionoi" the PF
10
J
4.J.4 Piaclical
С
tins id t'r^ turns
.m J
Properties of
Fart kle
Filter UU
\A
Su
men
а су І ІЗ
4 5
Bibliographical
Ксшлгкѕ
114
4
b
LxerüiseH 115
Robot
Motton
117
c>
1
lni
md
uč
tion
11"
^
2
['reliiiiniarifb
llfî
\2
1
K
menu tic
C
on figura lion lití
І.2 2
Pnibdbilistk Kiiieiiuiifs
11*?
5.3
Velocily Moinm
Міч
-Jel
Ł21
5-3
I Closed horrn Cikuidlum
121
5 3.2
Sampling
.\l^<ir¡Ilim
122
5.3.3
Mathematica]
Doi
ιν,ιΗοη
ot
trii' Vtjlntit\
Motion
M
od
ol
125
τ
4
Od
umet
r v
Motion
Model
132
■>.4
1
Closed Form Calculation
133
[■4 2
S.nmplinj'
Al^ìnthm
137
5 43
\I,ithi'm,ìli(.ìl
ľJenv^tion
of the Odometty Motion
Model
147
5.5
Motion and
M.ip^ 14li
5.6
Summary
14^
5.7
Biblio^rLiphica]
Remai
кь
345
145
6
iitiboi Perception
14У
ή
I Intruduttwn
1 №
fi
2
Млрч
ľí2
ћ
?'
fíťiim Mtidťls
of kanjţe
E iiider1· 151i
6 ?.
І І
hi.'
finsk'
Mľ.isurenient
Aljţiinthm L53
6 3 2
Adjusting
thľ
IntrmsK Model 1'drdmeters 15b
6.3.3
Mathoma
tical
Поті
гм
h
on tit the fJfjni Model
162
β Ί.4
Practicai
Con si derations
167
6 3.5
Limitations
ol
the Beam
Model
L
ЬН
6.4
Likelihood Fields for
Rúti
go
Hnders
169
6.4.1
Hasú
Algorithm
1
6У
6.4.2
Fx tensions
172
6.1!
Correla
(ion-Rised Measurement Models
174
6-6
Feature-Based
M l'apurement
Models
17<>
6-6 1
Feature
Ľxlraction
17ñ
6-0.2 [
jTi
dma
ґк
Measurements
177
fr.6.3 Sensor Model with Known Correspondence
178
ñ.íi
Λ
Samp\m% Poses
179
Ŕ.ťi
5
Further
ConsidťrjlH'ľis
ISO
ti.7 Prat titdl Considerations
1Й2
6.& Summdry IS3
6Я
Bibliographical Remarks
184
6.10
Exercise
ІЙ5
IT
Lútľalization T
89
Locali
zu!
ion:
Markov and
Gaussî û
ti
191
7.1
A
Taxonomy of
Loca
І і
za
(ion Problems
193
7.2
Markov
І.пнгаІі-^Іюп
197
7.3
Illusii-aiion ofMarko\ Locíilizalion
200
7.4
hKP
Localization
201
7.4 1
Illustration
201
7.4 2
"Піє
EKF
Localisation Algorithm
203
7.4.3
Mathemdtkđl
Derivation of
hKť
Localization
20^
7.4.4
Physical I rap
lenienia
t
ion
21(1
7
^ Estimating Correspondences
213
7.'i
1
EKF
Localiřatinn
with Unknown
Cot respond
en ce1!
215
7^2
Ma (hem ,i
tirül
Deri\ dtion of the ML Udtd
Association
2
Hi
7.6
Multi-I lypolhe&is Tracking
218
7.7
UKh I.wdlization
220
7.7Л
Malhenidtical Derivation or LK1: Localization
220
7 7.2
Illustration
223
7.8
ľríiťlical
ConsidcraEums 224?
Conicii!--
7 9
Summary
232
7
IO lìibliogmphioiL
Remarks
233
7.Г1
Exercises
234
Mobile Robot Lofiilization. Grid
Aud
Monte
Cario
237
8 1
Introduction
237
M-2 Grid
Locíi
Liza
tion
238
8.2.1
Basic Algorithm 23&
8.2.2
Grid Resolutions
234
8
2.Л
Computai
muai
Considerations
243
Л
2.4
]llu4lr.ìluin
24.^
8 3
Mon
U:
Crii lo
Localization
250
S3 1
Illustration
230
8.Л
2
Tlie
MCL Algorithm
2ï2
8.3
"ΐ
!łhjsu'jl I
mp
Iŕm
і'п
Iuti
un s
ľi.i
S.3 4
[Vopcriics
oí
MCL
253
3.3.5
Randám
Particle MCL. Recovery from
Fdilurť^
2^6
M.
3.6
.Modifying the
Jłropoh*łt
Distribution Zl>l
H
'M
KLD-Sdinplmj^:
Ad^pling
lhe
Si/e <it
Sample
Ы'Ль
263
H.4
Lofjli/.űtion in
ЈЗулдгшс
Hnvironmciiti?
26"
tí
5
ľiactical
Considerations
27Ъ
3.6
Summary
274
8.7
Bibliographical ReiiMrLs
275
8
H hxernsf's
27<i
III Mapping
279
Mapping
281
4.1
Introduction
281
Ч
2
I he
Otťupjnry Cind
Mapping Alj^iintbm
284
4.2.1
M
ult
і
-Sensor Fusion
293
4.3
Learning Inverse Measurement Models
294
'Λ3
1
Inverting the Meusuri'mi'Dl Modi'J
244
4.3 2
S-imphn^
írom
the- Korward Model
295
93 3
The Frror Function
2%
9.3.4
Examples and Furlher Considerations
9.4
Maximum
A Postenou
Occupancy Mapping
У
4 1
I he Cjse fur Maintaining l^'pi'niJennes
XII
4 4 2
(.'сеч
poney
í.ii id M.ippmg ivLfh
Forward Models
30
Я
5
Summary
304
Я.в
Bibliograph]
cal
Remarks
305
4.7
Extfd^
307
10
Siiftuìtnticous
Localization ami
Maf/ping
ЗАЧ
Ltì.l
lntrodnctinn
304
IQ
2
SLAM with Emended
Kalman
Filíc-is
312
1(1.2.1
Sel
и
ρ ,ι η
d
Assumptions
ül
2
1(1 2.2
SLAM wilts
Китч
n Cttrrťspimdťnrť
.-îl
'■
](] 2.3
M.itrn'm.itujl
L^nvatmn uf hKl·
Ы
AM
417
10 3
hKFSl
AM with Unknown Co
rrc
sponden
α.1*
323
10 3 1
rht'LioriLriil
FKK
bl
AM Alpontlim 323
10.3.2
Ехлтріеч
324
IÛ.3.3
Fra
I u ľŕ
Sciceli
o n and
Мдр
Management 32i
10.4
S u
mnu
Γ)
_ł_łlł
10.5
Bibluïjçraplutjl
Remarks
Ì.12
334
11
The GrnphbLAM Algorithm
337
11.1
JntRiduLtioTi
ЗЛ7
11.2
Intinti ve
Desť-nptLim
340
11 2.1
ISiiiidintf
Lptliľ(,ľ,iph
340
11 2.2
Jnfcrcnt-c
Я4^
I І
3
Tlu'CiHiphSI AM Algoiitbin
34ο
11.4
Ma
Üiema
li ca]
Derivation of
Gra
phS LA
M
353
11.4.1
The Full
SLAM
1\>ч
tenor JSJ
11.4 2
[he
Ni^.itHi'
I oj; l'tistcrun
ЗЇ4
Γ
1.4 1
Liylor F\p.iiisi(>n
^^
1
1.4 4
Cansłr^ctmg
tlu.' I n to
гт
.iti
ο η Κοητι
357
І
1-4.5
Kod
lining
tlu·
]
η Ιοί
in a I ion Form 36U
I I
4 !■>
Recovťťmfc
the T.ith and the Map
361
1
1-5
Data Association in UraphSLAM
362
11.5.1
The GraphSLAM Al^onthni
w
úh
Unknown
Correspondence 3(i3
11.
Ч.2
\l
л
tlie
піл
Cil
л
1
De
π
v
ľi
ti
ш
ι
nf tlie
CorresptniJeiitt1
Test \bb
II
ŕ>
Kftif](łnt
y t.ťsnsidiT.jtum
368
1 1 7
l· m p L n
с л
1
Inipľľiiiľiif.itiiiii
370
] 1
H
AherridtL' e
Opt
ι
inizii
ion Techniques 37b
11 9
Sumnurv
Ì7<J
]]
Ш
lÌLbhiì^r.iphitdl Keindrks
ЛН1
11
І І Ь'хсгсіч'ч
Ì82
12
Tlir Sparge
Extended
in
f
ormat ioti
hütet 385
12.1
Introdurrmi!
ΙΗί
[S
2
Intuitive
[ÏL'soiplion
ISft
12.3
The
SEIF SLAM
Alfiorilhm
391
12.4
Miithomo
honi
Ιλτιν,ιΐιοη
of EhobKIl·
ЧУЅ
12.4.1
Моііші
Updaio
395
12.4.2
Mť^uiĽiiiĽni
Updatet 39S
12
ť Spdr^LŕiÍLitJun
ј4,Ч
12^
J CiTi.T.il IJim
№
ПЯ
2
Sp.-iisLÍir.itiim m Shll·^
4(1(1
32.53
Mathematical
JX'riv.iIion
л
ŕ thi.'Sp,iľsifir,iti[in 4IH
12.5
Amortized
Appmxiinalc
Map Recovery
-Ш2
12 7
Huw
Sparse ShirnUI SELFs Be?
4Ü5
12
H
Incremental
Dati» Associ
¿»(ion
40^
12.8 1
Cnnipuling l[n.renieiii,il Data Associcilion
PmbđbilitLtiS
409
12.8
Z ľr,jLtkj]
QmsidtirJiLoib
411
І2.Ч
|tmneh-rTnd-f&£)Ljrn.J
Ії.ііл
Λ
sst jí
i j
tion
41т
12.
Ч І
iíecurMveS.Mrch 4lf»
12.9 2
Computing Arbitrary Data
Associo
t
ion
Probabilities»
416
12.
'í J
El|ul\
a lence
Constrímits
419
12.10
ľrui
tuai
Considera
tians 42U
12 11 MuJti-Kübot SLAM 4Z4
12 11.1
Intťgratmg M.ips
424
12
I 1
.2
MíHiemíitiCíil
І Хтнчттюп
tjf \1^p Inlejtr^tiím
427
12.11.3
ĽslablĹuhing
С
ci
r
respon
dc na.·
429
1211.4
example
429
12 12
Summary
412
12
ІЗ
Uiblto^ľ.iphiail
Кутплгкѕ
4.14
12 14
FxLTk-i^s
13
The FíistSLAM
Algorithm
437
ì*
1
"Hit Bd^Lt.
Alüoriihm
4
У)
X¡V
13.2
Factoring
lhe
SLAM Posterior
13.2Л
Mathematical Derivation of
lhe
ł-dLtored
SLAM
Posterior
442
13.3 histSLAM
with Known Datd
Association
444
13 4
Impubi
n g
th
ť
Proposal Distribution
451
13 4.1
Extending
lhe
Path Posterior by Sampling a Ncvv
Ним
451
13 4.2
Updating the Observed Feature
Eslimale
41^4
13 4.3
С.Л
Ic-uldting importance Factors
455
13 5
Unknown Data Assoridtion
457
13 6
Мдр
Mjnđ
gement
45^
13 7
The PaslSLAM Algorithms 46U
13
H Efŕicitnl
Implementa
lion
460
13 9
FdstSLAM for Feature-Based Maps 46rt
139.1
Empirical InsiRhts
468
13.9.2
Loop Closure
471
13 10
Grid-based FaslSLAM
474
13.10.1
Піе
Algorithm
474
13.10.2
Empirical Insights
475
13.11
Summary
479
13.12
Bibliographical Remarks
481
13.13
Exercises
482
IV Planning and Control
485
14
Marknv Decision Pracesseb
487
14.1
Motivation
487
14.2
Uncertainty in Action Selection
14.3
Value Iteration
495
14.3.1
Goals
,
-indP.iyoíi
4Ч5
14.3.2
Finding Optima] Control Polirii's for the Fully
143-3
Computing the \'j]ue Function
501
14.4
Application to Robot Control
503
14.
S
Siimm^iy
477
14.6
Bibliogr.iphic-il
Rťnurks
5l>J
14.7
15
Půrtiaily
Observable
Markov Decision Processes
513
1Ť
I Motivation
"ΐΠ
15 2
Λη
]
Ι
lustra ti ve hxdmpJe
тГт
15.2 1
Setup
^ľi
15.2.2
Control Choice 5\fi
15-2.3
Sensing
õl1'
15.2.4
Prediction
523
14 2 4
Deep Horizons and Pruning
526
15.Л
lhe
Tinite World POMDP Algorithm
527
15.4
Mathematical Derivation
oí
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
ľi^cÍicil
Considerations
4Ή>
156
ííiimmarv
541
15.7
Bibi
ι
ogra
рћ
¡Ctil
Reni-irk^
542
15.8
Exercises
544
16
Approximate POMDP Techniques
547
\h I Motivation
547
Ita.
2
QMDPs
544
16 3
Augmented Markov
Deci
sum Prt Hisses
16.3.1
The Augmented btjtc
Space
16 3.2
The AMDP Algorithm
551
lf>
3 1
Mdthtímatical
Derivation of AMDPs
553
16 3 4
Application
to Mobile Robot Navigation
556
16.4
MtmteCdrltjPOMDPb 551*
lii
4.1
Uhing Particle Sets
559
№ 4.2
l"he NfC-POMDP Algorithm
559
Hi
4.3
Vlathfrnuititdl Derivation af MC-POMDPs
562
lfi
4.4
]јгјг1кј1
Considerations
165
Summaiy
56^
là.
6
Bibliographical
Кетлгкь
5bb
Hi.
7
Е че
reiser
566
17
fïjj/ürdrjüfl
569
17.1
Introduction
569
17.2
Basic Explosion Algoiithm^,
571
17.2.1
Inforni a
(ion Gam
571
OJfJf'N-
172.2
Greedy Techniques
572
172 3
Montr Carlo Exploration
573
172.4
M
ulii
-Sic-
p
Techniques
575
173
Active I .ocdili/olion
573
I
7 4
FxploiMdon
loi
I earning
Осоірлік
\
Cind
Млрч
174
J t
(nnpuiinjţ
liitiirni.itJiiTi
Culi
π
'ïMll
]7.4
2
Propnuli
t
ιη^
C
ni in Wi
17.4 1
hxtľnsuin
to Multi-Kiibol Systfin1- ^
17
^ t-xploration
rar
SI. AM
WÌ
17.
τ
1
E-ntrí)p\
IJi'(
timposituìii in Si
Λ
M
^9
17.
τ
2
Exploration
m
E^istSLAM
^91
17.^
"ì
ť
m
pi
n
ci
I Ch.jr.K tiTi/titiim
^'Ж
I7.<i Siinimar)
ťiíJÍJ
17.7
Biblu^rLiphiLcj]
Rdiiurks tiO2
178
Exercises
ЫЧ
Index
Ь39 |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Thrun, Sebastian 1967- Burgard, Wolfram 1961- Fox, Dieter 1966- |
| author_GND | (DE-588)112396682 (DE-588)112720196 (DE-588)120913933 |
| author_facet | Thrun, Sebastian 1967- Burgard, Wolfram 1961- Fox, Dieter 1966- |
| author_role | aut aut aut |
| author_sort | Thrun, Sebastian 1967- |
| author_variant | s t st w b wb d f df |
| building | Verbundindex |
| bvnumber | BV035021895 |
| callnumber-first | T - Technology |
| callnumber-label | TJ211 |
| callnumber-raw | TJ211 |
| callnumber-search | TJ211 |
| callnumber-sort | TJ 3211 |
| callnumber-subject | TJ - Mechanical Engineering and Machinery |
| classification_rvk | ST 308 ZQ 6250 ZQ 6230 |
| classification_tum | DAT 774f DAT 815f MSR 632f FER 986f |
| ctrlnum | (OCoLC)265904143 (DE-599)BSZ285293737 |
| dewey-full | 629.892 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 629 - Other branches of engineering |
| dewey-raw | 629.892 |
| dewey-search | 629.892 |
| dewey-sort | 3629.892 |
| dewey-tens | 620 - Engineering and allied operations |
| discipline | Informatik Fertigungstechnik Mess-/Steuerungs-/Regelungs-/Automatisierungstechnik Mess-/Steuerungs-/Regelungs-/Automatisierungstechnik / Mechatronik |
| discipline_str_mv | Informatik Fertigungstechnik Mess-/Steuerungs-/Regelungs-/Automatisierungstechnik Mess-/Steuerungs-/Regelungs-/Automatisierungstechnik / Mechatronik |
| format | Book |
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| id | DE-604.BV035021895 |
| illustrated | Illustrated |
| index_date | 2024-07-02T21:46:38Z |
| indexdate | 2025-02-25T04:01:16Z |
| institution | BVB |
| isbn | 9780262201629 0262201623 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-016691001 |
| oclc_num | 265904143 |
| open_access_boolean | |
| owner | DE-20 DE-860 DE-29T DE-83 DE-11 DE-92 DE-634 DE-M347 DE-863 DE-BY-FWS DE-355 DE-BY-UBR DE-739 DE-19 DE-BY-UBM DE-B768 DE-384 DE-91 DE-BY-TUM DE-573 DE-706 DE-1043 DE-522 DE-188 DE-473 DE-BY-UBG DE-Aug4 DE-526 DE-703 DE-523 DE-859 DE-862 DE-BY-FWS DE-898 DE-BY-UBR DE-1050 DE-4325 |
| owner_facet | DE-20 DE-860 DE-29T DE-83 DE-11 DE-92 DE-634 DE-M347 DE-863 DE-BY-FWS DE-355 DE-BY-UBR DE-739 DE-19 DE-BY-UBM DE-B768 DE-384 DE-91 DE-BY-TUM DE-573 DE-706 DE-1043 DE-522 DE-188 DE-473 DE-BY-UBG DE-Aug4 DE-526 DE-703 DE-523 DE-859 DE-862 DE-BY-FWS DE-898 DE-BY-UBR DE-1050 DE-4325 |
| physical | XX, 647 Seiten Illustrationen, Diagramme, Karten |
| publishDate | 2006 |
| publishDateSearch | 2006 |
| publishDateSort | 2006 |
| publisher | MIT Press |
| record_format | marc |
| series2 | Intelligent robotics and autonomous agents |
| spellingShingle | Thrun, Sebastian 1967- Burgard, Wolfram 1961- Fox, Dieter 1966- Probabilistic robotics Probabilistischer Algorithmus (DE-588)4504622-0 gnd Bayes-Verfahren (DE-588)4204326-8 gnd Unsicheres Schließen (DE-588)4361044-4 gnd CAR Roboter (DE-588)4204399-2 gnd Roboter (DE-588)4050208-9 gnd |
| subject_GND | (DE-588)4504622-0 (DE-588)4204326-8 (DE-588)4361044-4 (DE-588)4204399-2 (DE-588)4050208-9 |
| title | Probabilistic robotics |
| title_auth | Probabilistic robotics |
| title_exact_search | Probabilistic robotics |
| title_exact_search_txtP | Probabilistic robotics |
| title_full | Probabilistic robotics Sebastian Thrun ; Wolfram Burgard ; Dieter Fox |
| title_fullStr | Probabilistic robotics Sebastian Thrun ; Wolfram Burgard ; Dieter Fox |
| title_full_unstemmed | Probabilistic robotics Sebastian Thrun ; Wolfram Burgard ; Dieter Fox |
| title_short | Probabilistic robotics |
| title_sort | probabilistic robotics |
| topic | Probabilistischer Algorithmus (DE-588)4504622-0 gnd Bayes-Verfahren (DE-588)4204326-8 gnd Unsicheres Schließen (DE-588)4361044-4 gnd CAR Roboter (DE-588)4204399-2 gnd Roboter (DE-588)4050208-9 gnd |
| topic_facet | Probabilistischer Algorithmus Bayes-Verfahren Unsicheres Schließen CAR Roboter Roboter |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016691001&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016691001&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016691001&sequence=000006&line_number=0003&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT thrunsebastian probabilisticrobotics AT burgardwolfram probabilisticrobotics AT foxdieter probabilisticrobotics |
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| Signatur: |
2000 ST 308 T531 |
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| Exemplar 1 | ausleihbar Checked out – Rückgabe bis: 29.09.2025 Vormerken |
THWS Würzburg Teilbibliothek SHL, Raum I.2.11
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1340 ST 308 T531st |
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| Exemplar 1 | nicht ausleihbar Verfügbar Bestellen |