SIMULTANEOUS LOCALIZATION AND MAPPING

Exactly Sparse Information Filters

By: WANG ZHAN

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Hardcover | 2 June 2011

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208 Pages

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22.86 x 15.24 x 1.27

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Simultaneous localization and mapping (SLAM) is a process where an autonomous vehicle builds a map of an unknown environment while concurrently generating an estimate for its location. This book is concerned with computationally efficient solutions to the large scale SLAM problems using exactly sparse Extended Information Filters (EIF).The invaluable book also provides a comprehensive theoretical analysis of the properties of the information matrix in EIF-based algorithms for SLAM. Three exactly sparse information filters for SLAM are described in detail, together with two efficient and exact methods for recovering the state vector and the covariance matrix. Proposed algorithms are extensively evaluated both in simulation and through experiments.

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Preface	p. v
Acknowledgments	p. vii
Introduction	p. 1
The SLAM Problem and Its Applications	p. 2
Description of the SLAM Problem	p. 2
Applications of SLAM	p. 3
Summary of SLAM Approaches	p. 4
EKF/EIF based SLAM Approaches	p. 5
Other SLAM Approaches	p. 8
Key Properties of SLAM	p. 12
Observability	p. 12
EKF SLAM Convergence	p. 19
EKF SLAM Consistency	p. 34
Motivation	p. 41
Book Overview	p. 43
Sparse Information Filters in SLAM	p. 47
Information Matrix in the Full SLAM Formulation	p. 47
Information Matrix in the Conventional EIF SLAM Formulation	p. 52
Meaning of Zero Off-diagonal Elements in Information Matrix	p. 54
Conditions for Achieving Exact Sparseness	p. 57
Strategies for Achieving Exact Sparseness	p. 59
Decoupling Localization and Mapping	p. 59
Using Local Submaps	p. 60
Combining Decoupling and Submaps	p. 60
Important Practical Issues in EIF SLAM	p. 61
Summary	p. 62
Decoupling Localization and Mapping	p. 63
The D-SLAM Algorithm	p. 64
Extracting Map Information from Observations	p. 64
Key Idea of D-SLAM	p. 69
Mapping	p. 69
Localization	p. 71
Structure of the Information Matrix in D-SLAM	p. 77
Efficient State and Covariance Recover	p. 78
Recovery Using the Preconditioned Conjugated Gradient (PCG) Method	p. 80
Recovery Using Complete Cholesky Factorization	p. 82
Implementation Issues	p. 84
Admissible Measurements	p. 84
Data Association	p. 86
Computer Simulations	p. 88
Experimental Evaluation	p. 95
Experiment in a Small Environment	p. 95
Experiment Using the Victoria Park Dataset	p. 95
Computational Complexity	p. 99
Storage	p. 102
Localization	p. 102
Mapping
State and Covariance Recovery	p. 103
Consistency of D-SLAM	p. 107
Bibliographical Remarks	p. 108
Summary	p. 111
D-SLAM Local Map Joining Filter	p. 113
Structure of D-SLAM Local Map Joining Filter	p. 114
State Vectors	p. 115
Relative Information Relating Feature Locations	p. 116
Combining Local Maps Using Relative Information	p. 116
Obtaining Relative Location Information in Local Maps
Generating a Local Map	p. 117
Obtaining Relative Location Information in the Local Map	p. 118
Global Map Update	p. 122
Measurement Model	p. 122
Updating the Global Map	p. 122
Sparse Information Matrix	p. 124
Implementation Issues	p. 125
Robot Localization	p. 125
Data Association	p. 126
State and Covariance Recovery	p. 127
When to Start a New Local Map	p. 128
Computational Complexity	p. 128
Storage	p. 128
Local Map Construction	p. 129
Global Map Update	p. 129
Rescheduling the Computational Effort	p. 130
Computer Simulations	p. 130
Simulation in a Small Area	p. 130
Simulation in a Large Area	p. 134
Experimental Evaluation	p. 140
Bibliographical Remarks	p. 147
Summary	p. 149
Sparse Local Submap Joining Filter	p. 151
Structure of Sparse Local Submap Joining Filter	p. 152
Input to SLSJF - Local Maps	p. 153
Output of SLSJF - One Global Map	p. 154
Fusing Local Maps into the Global Map	p. 155
Adding X_(K+1)s^G into the Global Map	p. 155
Initializing the Values of New Features and _(K+1)e^G in the Global Map	p. 156
Updating the Global Map	p. 157
Sparse Information Matrix	p. 158
Implementation Issues	p. 159
Data Association	p. 160
State and Covariance Recovery	p. 162
Computer Simulations	p. 162
Experimental Evaluation	p. 169
Discussion	p. l69
Computational Complexity	p. 169
Zero Information Loss	p. 173
Tradeoffs in Achieving Exactly Sparse Representation	p. 174
Summary	p. 175
Proofs of EKF SLAM Convergence and Consistency	p. 177
Matrix Inversion Lemma	p. 177
Proofs of EKF SLAM Convergence	p. 178
Proofs of EKF SLAM Consistency	p. 181
Incremental Method for Cholesky Factorization of SLAM Information Matrix	p. 185
Cholesky Factorization	p. 185
Approximate Cholesky Factorization	p. 186
Bibliography	p. 189
Table of Contents provided by Ingram. All Rights Reserved.

SIMULTANEOUS LOCALIZATION AND MAPPING

Exactly Sparse Information Filters

At a Glance

Hardcover

Shipping

How to return your order

Defective items

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