Skip to main content
SpringerLink
Log in

Simultaneous Localization and Mapping

  • Reference work entry
Book cover Springer Handbook of Robotics

Abstract

This chapter provides a comprehensive introduction in to the simultaneous localization and mapping problem, better known in its abbreviated form as SLAM. SLAM addresses the problem of a robot navigating an unknown environment. While navigating the environment, the robot seeks to acquire a map thereof, and at the same time it wishes to localize itself using its map. The use of SLAM problems can be motivated in two different ways: one might be interested in detailed environment models, or one might seek to maintain an accurate sense of a mobile robotʼs location. SLAM serves both of these purposes.

We review three major paradigms of algorithms from which a huge number of recently published methods are derived. First comes the traditional approach, which relies on the extended Kalman filter (EKF) for representing the robotʼs best estimate. The second paradigm draws its intuition from the fact that the SLAM problem can be viewed as a sparse graph of constraints, and it applies nonlinear optimization for recovering the map and the robotʼs locations. Finally, we survey the particle filter paradigm, which applies nonparametric density estimation and efficient factorization methods to the SLAM problem. This chapter discusses extensions of these basic methods. It elucidates variants of the SLAM problem and proposes a taxonomy for the field. Relevant research is referenced extensively, and open research problems are discussed.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 309.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Abbreviations

EKF:

extended Kalman filter

EM:

expectation maximization

GPS:

global positioning system

IEEE:

Institute of Electrical and Electronics Engineers

RANSAC:

random sample consensus

SLAM:

simultaneous localization and mapping

References

  1. K.F. Gauss: Theoria Motus Corporum Coelestium (Theory of the Motion of the Heavenly Bodies Moving about the Sun in Conic Sections) (Little, Brown, and Co., Republished in 1857, and by Dover in 1963, 1809)

    Google Scholar 

  2. G. Konecny: Geoinformation: Remote Sensing, Photogrammetry and Geographical Information Systems (Taylor Francis, New York 2002)

    Google Scholar 

  3. C. Tomasi, T. Kanade: Shape and motion from image streams under orthography: A factorization method, Int. J. Comput. Vis. 9(2), 137–154 (1992)

    Article  Google Scholar 

  4. S. Soatto, R. Brockett: Optimal structure from motion: Local ambiguities and global estimates, Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR) (Santa Barbara 1998) pp. 282–288

    Google Scholar 

  5. S. Thrun, W. Burgard, D. Fox: Probabilistic Robotics (MIT, Cambridge 2005)

    MATH  Google Scholar 

  6. H. Durrant-Whyte, T. Bailey: Simultaneous localization and mapping: Part I, IEEE Robot. Autom. Mag. (2006) pp. 99–108

    Google Scholar 

  7. T. Bailey, H. Durrant-Whyte: Simultaneous localization and mapping: Part II, IEEE Robot. Autom. Mag. (2006) pp. 108–117

    Google Scholar 

  8. P. Cheeseman, P. Smith: On the representation and estimation of spatial uncertainty, Int. J. Robot. 5, 56–68 (1986)

    Article  Google Scholar 

  9. R.C. Smith, P. Cheeseman: On the representation and estimation of spatial uncertainty, Int. J. Robot. Res. 5(4), 56–68 (1986)

    Article  Google Scholar 

  10. R. Smith, M. Self, P. Cheeseman: Estimating uncertain spatial relationships in robotics, Autonomous Robot Vehicles, ed. by I.J. Cox, G.T. Wilfong (Springer, Berlin, Heidelberg 1990) pp. 167–193

    Google Scholar 

  11. P. Moutarlier, R. Chatila: An experimental system for incremental environment modeling by an autonomous mobile robot, 1st International Symposium on Experimental Robotics (Montreal 1989)

    Google Scholar 

  12. P. Moutarlier, R. Chatila: Stochastic multisensory data fusion for mobile robot location and environment modeling, 5th Int. Symposium on Robotics Research (Tokyo 1989)

    Google Scholar 

  13. A.M. Jazwinsky: Stochastic Processes and Filtering Theory (Academic, New York 1970)

    Google Scholar 

  14. R.E. Kalman: A new approach to linear filtering and prediction problems, Trans. ASME J. Basic Eng. 82, 35–45 (1960)

    Google Scholar 

  15. P.S. Maybeck: The Kalman filter: An introduction to concepts. In: Autonomous Robot Vehicles, ed. by I.J. Cox, G.T. Wilfong (Springer, Berlin, Heidelberg 1990)

    Google Scholar 

  16. M. Csorba: Simultaneous Localisation and Map Building. Ph.D. Thesis (University of Oxford, Oxford 1997)

    Google Scholar 

  17. J. Neira, J.D. Tardós: Data association in stochastic mapping using the joint compatibility test, IEEE Trans. Robot. Autom. 17(6), 890–897 (2001)

    Article  Google Scholar 

  18. J. Neira, J.D. Tardós, J.A. Castellanos: Linear time vehicle relocation in SLAM, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) (Taiwan 2003)

    Google Scholar 

  19. T. Bailey: Mobile Robot Localisation and Mapping in Extensive Outdoor Environments. Ph.D. Thesis (University of Sydney, Sydney 2002)

    Google Scholar 

  20. G. Dissanayake, P. Newman, S. Clark, H.F. Durrant-Whyte, M. Csorba: A solution to the simultaneous localisation and map building (SLAM) problem, IEEE Trans. Robot. Autom. 17(3), 229–241 (2001)

    Article  Google Scholar 

  21. G. Dissanayake, S.B. Williams, H. Durrant-Whyte, T. Bailey: Map management for efficient simultaneous localization and mapping (SLAM), Autonom. Robot. 12, 267–286 (2002)

    Article  MATH  Google Scholar 

  22. S. Williams, G. Dissanayake, H.F. Durrant-Whyte: Constrained initialization of the simultaneous localization and mapping algorithm, Proceedings of the Symposium on Field and Service Robotics (Helsinki 2001)

    Google Scholar 

  23. J.J. Leonard, R.J. Rikoski, P.M. Newman, M. Bosse: Mapping partially observable features from multiple uncertain vantage points, Int. J. Robot. Res. 21(10), 943–975 (2002)

    Article  Google Scholar 

  24. A.J. Davison: Real-Time Simultaneous Localisation and Mapping with a Single Camera, International Conference on Computer Vision (Nice 2003) pp. 1403–1410

    Google Scholar 

  25. J.M.M. Montiel, J. Civera, A.J. Davison: Unified inverse depth parametrization for monocular SLAM, Proc. of the Robotics Science and Systems Conference (RSS06), Vol. 1 (Philadelphia 2006)

    Google Scholar 

  26. M. Bosse, P. Newman, J. Leonard, S. Teller: Simultaneous localization and map building in large-scale cyclic environments using the atlas framework, Int. J. Robot. Res. 23(12), 1113–1139 (2004)

    Article  Google Scholar 

  27. J. Nieto, T. Bailey, E. Nebot: Scan-SLAM: Combining ekf-slam and scan correlation, Proc. IEEE Int. Conf. Robotics and Automation (Barcelona 2005)

    Google Scholar 

  28. J. Folkesson, H.I. Christensen: Outdoor exploration and slam using a compressed filter, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) (Taiwan 2003) pp. 419–427

    Google Scholar 

  29. J. Guivant, E. Nebot: Optimization of the simultaneous localization and map building algorithm for real time implementation, IEEE Trans. Robot. Autom. 17(3), 242–257 (2001)

    Article  Google Scholar 

  30. J.J. Leonard, H.J.S. Feder: A computationally efficient method for large-scale concurrent mapping and localization, Proceedings of the Ninth International Symposium on Robotics Research, ed. by J. Hollerbach, D. Koditschek (Salt Lake City 1999)

    Google Scholar 

  31. S.B. Williams, G. Dissanayake, H. Durrant-Whyte: An efficient approach to the simultaneous localisation and mapping problem, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) (Washington 2002) pp. 406–411

    Google Scholar 

  32. J.D. Tardós, J. Neira, P.M. Newman, J.J. Leonard: Robust mapping and localization in indoor environments using sonar data, Int. J. Robot. Res. 21(4), 311–330 (2002)

    Article  Google Scholar 

  33. S. Betgé-Brezetz, R. Chatila, M. Devy: Object-based modelling and localization in natural environments, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) (Osaka 1995)

    Google Scholar 

  34. S. Betgé-Brezetz, P. Hébert, R. Chatila, M. Devy: Uncertain map making in natural environments, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) (Minneapolis 1996)

    Google Scholar 

  35. J.E. Guivant, E.M. Nebot, J. Nieto, F. Masson: Navigation and mapping in large unstructured environments, Int. J. Robot. Res. 23(4), 449–472 (2004)

    Article  Google Scholar 

  36. J. Nieto, J.E. Guivant, E.M. Nebot: The hybrid metric maps (HYMMs): A novel map representation for dense SLAM, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) (New Orleans 2004)

    Google Scholar 

  37. W. Burgard, D. Fox, H. Jans, C. Matenar, S. Thrun: Sonar-based mapping of large-scale mobile robot environments using EM, Proceedings of the International Conference on Machine Learning (Bled 1999)

    Google Scholar 

  38. H. Shatkay, L. Kaelbling: Learning topological maps with weak local odometric information, Proceedings of IJCAI-97 (Nagoya 1997)

    Google Scholar 

  39. S. Thrun, D. Fox, W. Burgard: A probabilistic approach to concurrent mapping and localization for mobile robots, Machine Learn. 31, 29–53 (1998), Also appeared in Autonomous Robots 5, 253–271 (joint issue)

    Article  MATH  Google Scholar 

  40. A.P. Dempster, A.N. Laird, D.B. Rubin: Maximum likelihood from incomplete data via the EM algorithm, J. R. Statist. Soc. Ser. B 39(1), 1–38 (1977)

    MATH  MathSciNet  Google Scholar 

  41. H.F. Durrant-Whyte: Uncertain geometry in robotics, IEEE Trans. Robot. Autom. 4(1), 23–31 (1988)

    Article  Google Scholar 

  42. F. Lu, E. Milios: Globally consistent range scan alignment for environment mapping, Autonom. Robot. 4, 333–349 (1997)

    Article  Google Scholar 

  43. F. Dellaert: Square root SAM, Proceedings of the Robotics Science and Systems Conference, ed. by S. Thrun, G. Sukhatme, S. Schaal, O. Brock (Cambridge 2005)

    Google Scholar 

  44. T. Duckett, S. Marsland, J. Shapiro: Learning globally consistent maps by relaxation, Proceedings of the IEEE International Conference on Robotics and Automation (San Francisco 2000) pp. 3841–3846

    Google Scholar 

  45. T. Duckett, S. Marsland, J. Shapiro: Fast, on-line learning of globally consistent maps, Auton. Robot. 12(3), 287–300 (2002)

    Article  MATH  Google Scholar 

  46. J. Folkesson, H.I. Christensen: Graphical SLAM: A self-correcting map, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) (New Orleans 2004)

    Google Scholar 

  47. J. Folkesson, H.I. Christensen: Robust SLAM, Proceedings of the International Symposium on Autonomous Vehicles (Lisboa 2004)

    Google Scholar 

  48. U. Frese, G. Hirzinger: Simultaneous localization and mapping – a discussion, Proceedings of the IJCAI Workshop on Reasoning with Uncertainty in Robotics (Seattle 2001) pp. 17–26

    Google Scholar 

  49. U. Frese, P. Larsson, T. Duckett: A multigrid algorithm for simultaneous localization and mapping, IEEE Trans. Robot. 21(2), 196–207 (2005)

    Article  Google Scholar 

  50. M. Golfarelli, D. Maio, S. Rizzi: Elastic correction of dead-reckoning errors in map building, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (Victoria 1998) pp. 905–911

    Google Scholar 

  51. K. Konolige: Large-scale map-making, Proceedings of the AAAI National Conference on Artificial Intelligence (San Jose 2004) pp. 457–463

    Google Scholar 

  52. M. Montemerlo, S. Thrun: Large-scale robotic 3-d mapping of urban structures, Proceedings of the International Symposium on Experimental Robotics (ISER) (Singapore 2004)

    Google Scholar 

  53. Y. Liu, S. Thrun: Results for outdoor-SLAM using sparse extended information filters, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) (Taiwan 2003)

    Google Scholar 

  54. M.A. Fischler, R.C. Bolles: Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography, Commun. ACM 24, 381–395 (1981)

    Article  MathSciNet  Google Scholar 

  55. D. Hähnel, W. Burgard, B. Wegbreit, S. Thrun: Towards lazy data association in SLAM, Proceedings of the 11th International Symposium of Robotics Research (ISRRʼ03) (Sienna 2003)

    Google Scholar 

  56. B. Kuipers, J. Modayil, P. Beeson, M. MacMahon, F. Savelli: Local metrical and global topological maps in the hybrid spatial semantic hierarchy, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) (New Orleans 2004)

    Google Scholar 

  57. S. Thrun, S. Thayer, W. Whittaker, C. Baker, W. Burgard, D. Ferguson, D. Hähnel, M. Montemerlo, A. Morris, Z. Omohundro, C. Reverte, W. Whittaker: Autonomous exploration and mapping of abandoned mines, IEEE Robot. Autom. Mag. 11(4), 79–91 (2004)

    Article  Google Scholar 

  58. A. Elfes: Sonar-based real-world mapping and navigation, IEEE J. Robot. Autom. RA-3(3), 249–265 (1987)

    Article  Google Scholar 

  59. H.P. Moravec: Sensor fusion in certainty grids for mobile robots, AI Mag. 9(2), 61–74 (1988)

    Google Scholar 

  60. T.M. Cover, J.A. Thomas: Elements of Information Theory (Wiley, New York 1991)

    Book  MATH  Google Scholar 

  61. P. Newman, J.L.R. Rikoski: Towards constant-time slam on an autonomous underwater vehicle using synthetic aperture sonar, Proceedings of the International Symposium of Robotics Research (Sienna 2003)

    Google Scholar 

  62. M.A. Paskin: Thin junction tree filters for simultaneous localization and mapping, Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence (IJCAI) (Acapulco 2003)

    Google Scholar 

  63. S. Thrun, D. Koller, Z. Ghahramani, H. Durrant-Whyte, A.Y. Ng: Simultaneous mapping and localization with sparse extended information filters, Proceedings of the Fifth International Workshop on Algorithmic Foundations of Robotics, ed. by J.-D. Boissonnat, J. Burdick, K. Goldberg, S. Hutchinson (Nice 2002)

    Google Scholar 

  64. E. Nettleton, S. Thrun, H. Durrant-Whyte: Decentralised slam with low-bandwidth communication for teams of airborne vehicles, Proceedings of the International Conference on Field and Service Robotics (Lake Yamanaka 2003)

    Google Scholar 

  65. P. Newman: On the Structure and Solution of the Simultaneous Localisation and Map Building Problem. Ph.D. Thesis (Australian Centre for Field Robotics, University of Sydney, Sydney 2000)

    Google Scholar 

  66. P.M. Newman, H.F. Durrant-Whyte: Geometric projection filter: An efficient solution to the SLAM problem, Proc. SPIE 4571 (2001)

    Google Scholar 

  67. S. Thrun, Y. Liu, D. Koller, A.Y. Ng, Z. Ghahramani, H. Durrant-Whyte: Simultaneous localization and mapping with sparse extended information filters, Int. J. Robot. Res. 23(7–8), 693–716 (2004)

    Article  Google Scholar 

  68. S.B. Williams: Efficient Solutions to Autonomous Mapping and Navigation Problems. Ph.D. Thesis (University of Sydney, Sydney 2001)

    Google Scholar 

  69. N. Metropolis, S. Ulam: The Monte Carlo method, J. Am. Stat. Assoc. 44(247), 335–341 (1949)

    Article  MATH  MathSciNet  Google Scholar 

  70. D.B. Rubin: Using the SIR algorithm to simulate posterior distributions, Bayesian Statistics 3, ed. by M.H. Bernardo, K.M. DeGroot, D.V. Lindley, A.F.M. Smith (Oxford Univ. Press, Oxford 1988)

    Google Scholar 

  71. A. Doucet: On sequential simulation-based methods for Bayesian filtering. Technical Report CUED/F-INFENG/TR 310 (Cambridge University, Cambridge 1998)

    Google Scholar 

  72. G. Kitagawa: Monte Carlo filter and smoother for non-Gaussian nonlinear state space models, J. Comput. Graph. Statist. 5(1), 1–25 (1996)

    Article  MathSciNet  Google Scholar 

  73. J. Liu, R. Chen: Sequential monte carlo methods for dynamic systems, J. Am. Stat. Assoc. 93, 1032–1044 (1998)

    Article  MATH  Google Scholar 

  74. M. Pitt, N. Shephard: Filtering via simulation: auxiliary particle filter, J. Am. Stat. Assoc. 94, 590–599 (1999)

    Article  MATH  MathSciNet  Google Scholar 

  75. D. Blackwell: Conditional expectation and unbiased sequential estimation, Ann. Math. Statist. 18, 105–110 (1947)

    Article  MATH  MathSciNet  Google Scholar 

  76. C.R. Rao: Information and accuracy obtainable in estimation of statistical parameters, Bull. Calcutta Math. Soc. 37, 81–91 (1945)

    MATH  MathSciNet  Google Scholar 

  77. K. Murphy, S. Russell: Rao-Blackwellized particle filtering for dynamic Bayesian networks. In: Sequential Monte Carlo Methods in Practice, ed. by A. Doucet, N. de Freitas, N. Gordon (Springer, Berlin, Heidelberg 2001) pp. 499–516

    Google Scholar 

  78. M. Montemerlo, S. Thrun, D. Koller, B. Wegbreit: FastSLAM: A factored solution to the simultaneous localization and mapping problem, Proceedings of the AAAI National Conference on Artificial Intelligence (Edmonton 2002)

    Google Scholar 

  79. J. Pearl: Probabilistic reasoning in intelligent systems: networks of plausible inference (Morgan Kaufmann, San Mateo 1988)

    Google Scholar 

  80. J. Guivant, E. Nebot, S. Baiker: Autonomous navigation and map building using laser range sensors in outdoor applications, J. Robot. Syst. 17(10), 565–583 (2000)

    Article  MATH  Google Scholar 

  81. D. Hähnel, D. Fox, W. Burgard, S. Thrun: A highly efficient FastSLAM algorithm for generating cyclic maps of large-scale environments from raw laser range measurements, Proceedings of the Conference on Intelligent Robots and Systems (IROS) (Las Vegas 2003)

    Google Scholar 

  82. A. Eliazar, R. Parr: DP-SLAM: Fast, robust simultaneous localization and mapping without predetermined landmarks, Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence (IJCAI) (Acapulco 2003)

    Google Scholar 

  83. A. Eliazar, R. Parr: DP-SLAM 2.0, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) (New Orleans 2004)

    Google Scholar 

  84. M. Montemerlo, S. Thrun, D. Koller, B. Wegbreit: FastSLAM 2.0: An improved particle filtering algorithm for simultaneous localization and mapping that provably converges, Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence (IJCAI) (Acapulco 2003)

    Google Scholar 

  85. R. van der Merwe, N. de Freitas, A. Doucet, E. Wan: The unscented particle filter. In: Adv. in Neural Inform. Process. Syst. 13 (2001)

    Google Scholar 

  86. D. Hähnel, D. Schulz, W. Burgard: Mobile robot mapping in populated environments, Autonom. Robot. 17(7), 579–598 (2003)

    Google Scholar 

  87. C.-C. Wang, C. Thorpe, S. Thrun: Online simultaneous localization and mapping with detection and tracking of moving objects: Theory and results from a ground vehicle in crowded urban areas, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) (Taiwan 2003)

    Google Scholar 

  88. D.F. Wolf, G.S. Sukhatme: Mobile robot simultaneous localization and mapping in dynamic environments, Autonom. Robot. 19(1), 53–65 (2005)

    Article  Google Scholar 

  89. J.-S. Gutmann, K. Konolige: Incremental mapping of large cyclic environments, Proceedings of the IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA) (2000)

    Google Scholar 

  90. E.W. Nettleton, P.W. Gibbens, H.F. Durrant-Whyte: Closed form solutions to the multiple platform simultaneous localisation and map building (slam) problem, Sensor Fusion: Architectures, Algorithms, and Applications IV, Vol. 4051, ed. by Bulur V. Dasarathy (Bellingham 2000) pp. 428–437

    Google Scholar 

  91. J. Fenwick, P. Newman, J. Leonard: Collaborative concurrent mapping and localization, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) (Washington 2002)

    Google Scholar 

  92. I.M. Rekleitis, G. Dudek, E.E. Milios: Multi-robot collaboration for robust exploration, Ann. Math. Artif. Intell. 31(1-4), 7–40 (2001)

    Article  Google Scholar 

  93. S. Thrun, Y. Liu: Multi-robot SLAM with sparse extended information filers, Proceedings of the 11th International Symposium of Robotics Research (ISRRʼ03) (Sienna 2003)

    Google Scholar 

  94. C. Frueh, A. Zakhor: Constructing 3d city models by merging ground-based and airborne views, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) (Madison 2003)

    Google Scholar 

  95. M. Devy, C. Parra: 3-d scene modelling and curve-based localization in natural environments, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) (Leuven 1998)

    Google Scholar 

  96. L. Iocchi, K. Konolige, M. Bajracharya: Visually realistic mapping of a planar environment with stereo, Proceesings of the 2000 International Symposium on Experimental Robotics (Waikiki 2000)

    Google Scholar 

  97. S. Teller, M. Antone, Z. Bodnar, M. Bosse, S. Coorg, M. Jethwa, N. Master: Calibrated, registered images of an extended urban area, Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR) (Kauai 2001)

    Google Scholar 

  98. R. Eustice, H. Singh, J. Leonard, M. Walter, R. Ballard: Visually navigating the RMS Titanic with SLAM information filters, Proceedings of the Robotics Science and Systems Conference, ed. by S. Thrun, G. Sukhatme, S. Schaal, O. Brock (Cambridge 2005)

    Google Scholar 

  99. R. Rikoski, J. Leonard, P. Newman, H. Schmidt: Trajectory sonar perception in the ligurian sea, Proceedings of the International Symposium on Experimental Robotics (ISER) (Singapore 2004)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science Artificial Intelligence Laboratory, Stanford University, 94305-9010, Stanford, CA, USA

    Sebastian Thrun Prof

  2. Department of Mechanical Engineering, Massachusetts Institute of Technology, 5-214 77 Massachusetts Ave, 02139, Cambridge, MA, USA

    John J. Leonard Prof

Authors
  1. Sebastian Thrun Prof
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. John J. Leonard Prof
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Sebastian Thrun Prof or John J. Leonard Prof .

Editor information

Editors and Affiliations

  1. PRISMA Lab, Dipartimento di Informatica e Sistemistica, Universitá degli Studi di Napoli Federico II, 80125, Napoli, Italy, Via Claudio 21

    Bruno Siciliano Prof.

  2. Artificial Intelligence Laboratory, Department of Computer Science, Stanford University, 94305-9010, Stanford, CA, USA

    Oussama Khatib Prof.

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag

About this entry

Cite this entry

Thrun, S., Leonard, J.J. (2008). Simultaneous Localization and Mapping. In: Siciliano, B., Khatib, O. (eds) Springer Handbook of Robotics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30301-5_38

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-30301-5_38

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23957-4

  • Online ISBN: 978-3-540-30301-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation