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Author Wang, Zhan Et
Title Simultaneous Localization and Mapping : Exactly Sparse Information Filters
Imprint Singapore : World Scientific Publishing Co Pte Ltd, 2011
©2011
book jacket
Descript 1 online resource (208 pages)
text txt rdacontent
computer c rdamedia
online resource cr rdacarrier
Series New Frontiers in Robotics Ser. ; v.3
New Frontiers in Robotics Ser
Note Intro -- Contents -- Preface -- Acknowledgments -- Chapter 1 Introduction -- 1.1 The SLAM Problem and Its Applications -- 1.1.1 Description of the SLAM Problem -- 1.1.2 Applications of SLAM -- 1.2 Summary of SLAM Approaches -- 1.2.1 EKF/EIF based SLAM Approaches -- 1.2.2 Other SLAM Approaches -- 1.3 Key Properties of SLAM -- 1.3.1 Observability -- 1.3.2 EKF SLAM Convergence -- 1.3.3 EKF SLAM Consistency -- 1.4 Motivation -- 1.5 Book Overview -- Chapter 2 Sparse Information Filters in SLAM -- 2.1 Information Matrix in the Full SLAM Formulation -- 2.2 Information Matrix in the Conventional EIF SLAM Formulation -- 2.3 Meaning of Zero Off-diagonal Elements in Information Matrix -- 2.4 Conditions for Achieving Exact Sparseness -- 2.5 Strategies for Achieving Exact Sparseness -- 2.5.1 Decoupling Localization and Mapping -- 2.5.2 Using Local Submaps -- 2.5.3 Combining Decoupling and Submaps -- 2.6 Important Practical Issues in EIF SLAM -- 2.7 Summary -- Chapter 3 Decoupling Localization and Mapping -- 3.1 The D-SLAM Algorithm -- 3.1.1 Extracting Map Information from Observations -- 3.1.2 Key Idea of D-SLAM -- 3.1.3 Mapping -- 3.1.4 Localization -- 3.2 Structure of the Information Matrix in D-SLAM -- 3.3 Efficient State and Covariance Recovery -- 3.3.1 Recovery Using the Preconditioned Conjugated Gradient (PCG) Method -- 3.3.2 Recovery Using Complete Cholesky Factorization -- 3.4 Implementation Issues -- 3.4.1 Admissible Measurements -- 3.4.2 Data Association -- 3.5 Computer Simulations -- 3.6 Experimental Evaluation -- 3.6.1 Experiment in a Small Environment -- 3.6.2 Experiment Using the Victoria Park Dataset -- 3.7 Computational Complexity -- 3.7.1 Storage -- 3.7.2 Localization -- 3.7.3 Mapping -- 3.7.4 State and Covariance Recovery -- 3.8 Consistency of D-SLAM -- 3.9 Bibliographical Remarks -- 3.10 Summary -- Chapter 4 D-SLAM Local Map Joining Filter
4.1 Structure of D-SLAM Local Map Joining Filter -- 4.1.1 State Vectors -- 4.1.2 Relative Information Relating Feature Locations -- 4.1.3 Combining Local Maps Using Relative Information -- 4.2 Obtaining Relative Location Information in Local Maps -- 4.2.1 Generating a Local Map -- 4.2.2 Obtaining Relative Location Information in the Local Map -- 4.3 Global Map Update -- 4.3.1 Measurement Model -- 4.3.2 Updating the Global Map -- 4.3.3 Sparse Information Matrix -- 4.4 Implementation Issues -- 4.4.1 Robot Localization -- 4.4.2 Data Association -- 4.4.3 State and Covariance Recovery -- 4.4.4 When to Start a New Local Map -- 4.5 Computational Complexity -- 4.5.1 Storage -- 4.5.2 Local Map Construction -- 4.5.3 Global Map Update -- 4.5.4 Rescheduling the Computational Effort -- 4.6 Computer Simulations -- 4.6.1 Simulation in a Small Area -- 4.6.2 Simulation in a Large Area -- 4.7 Experimental Evaluation -- 4.8 Bibliographical Remarks -- 4.9 Summary -- Chapter 5 Sparse Local Submap Joining Filter -- 5.1 Structure of Sparse Local Submap Joining Filter -- 5.1.1 Input to SLSJF - Local Maps -- 5.1.2 Output of SLSJF - One Global Map -- 5.2 Fusing Local Maps into the Global Map -- 5.2.1 Adding XG(k+1)s into the Global Map -- 5.2.2 Initializing the Values of New Features and XG(k+1)e in the Global Map -- 5.2.3 Updating the Global Map -- 5.3 Sparse Information Matrix -- 5.4 Implementation Issues -- 5.4.1 Data Association -- 5.4.2 State and Covariance Recovery -- 5.5 Computer Simulations -- 5.6 Experimental Evaluation -- 5.7 Discussion -- 5.7.1 Computational Complexity -- 5.7.2 Zero Information Loss -- 5.7.3 Tradeoffs in Achieving Exactly Sparse Representation -- 5.8 Summary -- Appendix A Proofs of EKF SLAM Convergence and Consistency -- A.1 Matrix Inversion Lemma -- A.2 Proofs of EKF SLAM Convergence -- A.3 Proofs of EKF SLAM Consistency
Appendix B Incremental Method for Cholesky Factorization of SLAM Information Matrix -- B.1 Cholesky Factorization -- B.2 Approximate Cholesky Factorization -- Bibliography
Description based on publisher supplied metadata and other sources
Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2020. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries
Link Print version: Wang, Zhan Et Simultaneous Localization and Mapping : Exactly Sparse Information Filters Singapore : World Scientific Publishing Co Pte Ltd,c2011 9789814350310
Subject Mobile robots.;Robots -- Control systems.;Sparse matrices.;Robotics.;Mappings (Mathematics)
Electronic books
Alt Author Huang, Shoudong
Dissanayake, Gamini
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