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Simultaneous Localization and Mapping

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Springer Handbook of Robotics

Part of the book series: Springer Handbooks ((SHB))

Abstract

This chapter provides a comprehensive introduction in to the simultaneous localization and mapping problem, better known in its abbreviated form as GlossaryTerm

SLAM

. SLAM addresses the main perception 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 robot’s location. SLAM serves both of these purposes.

We review the three major paradigms from which many published methods for SLAM are derived: (1) the extended Kalman filter (GlossaryTerm

EKF

); (2) particle filtering; and (3) graph optimization. We also review recent work in three-dimensional (GlossaryTerm

3-D

) SLAM using visual and red green blue distance-sensors (GlossaryTerm

RGB-D

), and close with a discussion of open research problems in robotic mapping.

figure a

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Abbreviations

2-D:

two-dimensional

3-D:

three-dimensional

DCS:

dynamic covariance scaling

DOF:

degree of freedom

EKF:

extended Kalman filter

fastSLAM:

fast simultaneous localization and mapping

GPS:

global positioning system

GPU:

graphics processing unit

ICP:

iterative closest point

PCL:

point cloud library

PTAM:

parallel tracking and mapping

RGB-D:

red green blue distance

SAM:

smoothing and mapping

SFM:

structure from motion

SGD:

stochastic gradient descent

SLAM:

simultaneous localization and mapping

SSA:

sparse surface adjustment

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Author information

Authors and Affiliations

  1. Institute for Geodesy and Geoinformation, University of Bonn, Nussallee 15, 53115, Bonn, Germany

    Cyrill Stachniss

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

    John J. Leonard

  3. Udacity Inc., 2465 Latham Street, 3rd Floor, CA 94040, Mountain View, USA

    Sebastian Thrun

Authors
  1. Cyrill Stachniss
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  2. John J. Leonard
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  3. Sebastian Thrun
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Corresponding author

Correspondence to Cyrill Stachniss .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, University of Naples Federico II, Via Claudio 21, 80125, Naples, Italy

    Bruno Siciliano

  2. Department of Computer Sciences, Artificial Intelligence Laboratory, Stanford University, 450 Serra Mall, CA 94305, Stanford, USA

    Oussama Khatib

Video-References

Video-References

:

Deformation-based loop closure for Dense RGB-D SLAM available from http://handbookofrobotics.org/view-chapter/46/videodetails/439

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Large-scale SLAM using the Atlas framework available from http://handbookofrobotics.org/view-chapter/46/videodetails/440

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Graph-based SLAM available from http://handbookofrobotics.org/view-chapter/46/videodetails/441

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Graph-based SLAM available from http://handbookofrobotics.org/view-chapter/46/videodetails/442

:

Graph-based SLAM available from http://handbookofrobotics.org/view-chapter/46/videodetails/443

:

Graph-based SLAM available from http://handbookofrobotics.org/view-chapter/46/videodetails/444

:

Graph-based SLAM available from http://handbookofrobotics.org/view-chapter/46/videodetails/445

:

Graph-based SLAM using TORO available from http://handbookofrobotics.org/view-chapter/46/videodetails/446

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Sparse pose adjustment available from http://handbookofrobotics.org/view-chapter/46/videodetails/447

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Pose graph compression for laser-based SLAM available from http://handbookofrobotics.org/view-chapter/46/videodetails/449

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Pose graph compression for laser-based SLAM available from http://handbookofrobotics.org/view-chapter/46/videodetails/450

:

Pose graph compression for laser-based SLAM available from http://handbookofrobotics.org/view-chapter/46/videodetails/451

:

DTAM: Dense tracking and mapping in real-time available from http://handbookofrobotics.org/view-chapter/46/videodetails/452

:

MonoSLAM: Real-time single camera SLAM available from http://handbookofrobotics.org/view-chapter/46/videodetails/453

:

SLAM++: Simultaneous localisation and mapping at the level of objects available from http://handbookofrobotics.org/view-chapter/46/videodetails/454

:

Extended Kalman filter SLAM available from http://handbookofrobotics.org/view-chapter/46/videodetails/455

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Stachniss, C., Leonard, J.J., Thrun, S. (2016). Simultaneous Localization and Mapping. In: Siciliano, B., Khatib, O. (eds) Springer Handbook of Robotics. Springer Handbooks. Springer, Cham. https://doi.org/10.1007/978-3-319-32552-1_46

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  • DOI: https://doi.org/10.1007/978-3-319-32552-1_46

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