LEADER 05874nam 2200757 a 450 001 9910788963203321 005 20230725052617.0 010 $a1-283-43379-6 010 $a9786613433794 010 $a981-4350-32-X 035 $a(CKB)3400000000016157 035 $a(EBL)3050909 035 $a(OCoLC)777330118 035 $a(SSID)ssj0000647634 035 $a(PQKBManifestationID)12226958 035 $a(PQKBTitleCode)TC0000647634 035 $a(PQKBWorkID)10593761 035 $a(PQKB)10997070 035 $a(MiAaPQ)EBC3050909 035 $a(WSP)00008145 035 $a(Au-PeEL)EBL3050909 035 $a(CaPaEBR)ebr10524592 035 $a(CaONFJC)MIL343379 035 $a(EXLCZ)993400000000016157 100 $a20110920d2011 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aSimultaneous localization and mapping$b[electronic resource] $eexactly sparse information filters /$fZhan Wang, Shoudong Huang, Gamini Dissanayake 210 $aSingapore ;$aHackensack, N.J. $cWorld Scientific$dc2011 215 $a1 online resource (208 p.) 225 1 $aNew frontiers in robotics ;$vv. 3 300 $aDescription based upon print version of record. 311 $a981-4350-31-1 320 $aIncludes bibliographical references (p. 180-194). 327 $aContents; 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 327 $a2.3 Meaning of Zero Off-diagonal Elements in Information Matrix2.4 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 327 $a3.3 Efficient State and Covariance Recovery3.3.1 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 327 $aChapter 4 D-SLAM Local Map Joining Filter4.1 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 327 $a4.4.4 When to Start a New Local Map4.5 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 327 $a5.2.1 Adding XG(k+1)s into the Global Map 330 $aSimultaneous localization and mapping (SLAM) is a process where an autonomous vehicle builds a map of an unknown environment while concurrently generating an estimate for its location. This book is concerned with computationally efficient solutions to the large scale SLAM problems using exactly sparse Extended Information Filters (EIF). The invaluable book also provides a comprehensive theoretical analysis of the properties of the information matrix in EIF-based algorithms for SLAM. Three exactly sparse information filters for SLAM are described in detail, together with two efficient and exact methods for recovering the state vector and the covariance matrix. Proposed algorithms are extensively evaluated both in simulation and through experiments. 410 0$aNew frontiers in robotics ;$vv. 3. 606 $aMobile robots 606 $aRobots$xControl systems 606 $aSparse matrices 606 $aRobotics 606 $aMappings (Mathematics) 615 0$aMobile robots. 615 0$aRobots$xControl systems. 615 0$aSparse matrices. 615 0$aRobotics. 615 0$aMappings (Mathematics) 676 $a629.892637 700 $aWang$b Zhan$01574170 701 $aHuang$b Shoudong$f1969-$01574171 701 $aDissanayake$b Gamini$01194459 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910788963203321 996 $aSimultaneous localization and mapping$93850280 997 $aUNINA