Simultaneous Localization and Mapping: Exactly Sparse Information Filters
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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Chapter 1 Introduction
Chapter 2 Sparse Information Filters in SLAM
Chapter 3 Decoupling Localization and Mapping
Chapter 4 DSLAM Local Map Joining Filter
Chapter 5 Sparse Local Submap Joining Filter
Appendix A Proofs of EKF SLAM Convergence and Consistency
Appendix B Incremental Method for Cholesky Factorization of SLAM Information Matrix
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associated covariance matrix atan2 based SLAM complete Cholesky Factorization computational cost conditional independence contains control noise coordinate frame corresponding D-SLAM Local Map data association decoupled denoted dimension of HR Dissanayake Durrant-Whyte environment evaluated exactly sparse information feature location estimates features f1 formation matrix fºx Frese full SLAM function fused global map global state vector graph Hmap inconsistency information loss information vector initial robot Jacobian landmark localization and mapping map estimate Map Joining Filter map update matrix inversion lemma non-zero elements nonlinear number of features observation model obtained particle filters preconditioner presented in Section Proc process model relative location information reordering robot end pose robot location estimate robot moves robot orientation uncertainty robot pose robot start pose simulation simultaneous localization SLAM algorithm SLAM problem SLAM process SLSJF sparse information filter sparse information matrix sparse matrix sparsification submatrix Tardos Theorem Thrun Time(sec traditional EKF SLAM