Simultaneous Localization and Mapping: Exactly Sparse Information Filters

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World Scientific, 2011 - Electronic books - 208 pages
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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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Contents

Chapter 1 Introduction
1
Chapter 2 Sparse Information Filters in SLAM
47
Chapter 3 Decoupling Localization and Mapping
63
Chapter 4 DSLAM Local Map Joining Filter
113
Chapter 5 Sparse Local Submap Joining Filter
151
Appendix A Proofs of EKF SLAM Convergence and Consistency
177
Appendix B Incremental Method for Cholesky Factorization of SLAM Information Matrix
185
Bibliography
189
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