Simultaneous Localization and Mapping

  • Cyrill StachnissEmail author
  • John J. Leonard
  • Sebastian Thrun
Part of the Springer Handbooks book series (SHB)


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 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 (EKF); (2) particle filtering; and (3) graph optimization. We also review recent work in three-dimensional (3-D ) SLAM using visual and red green blue distance-sensors (RGB-D), and close with a discussion of open research problems in robotic mapping.


Particle Filter Extended Kalman Filter Data Association Bundle Adjustment Robot Location 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.





dynamic covariance scaling


degree of freedom


extended Kalman filter


fast simultaneous localization and mapping


global positioning system


graphics processing unit


iterative closest point


point cloud library


parallel tracking and mapping


red green blue distance


smoothing and mapping


structure from motion


stochastic gradient descent


simultaneous localization and mapping


sparse surface adjustment


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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Cyrill Stachniss
    • 1
    Email author
  • John J. Leonard
    • 2
  • Sebastian Thrun
    • 3
  1. 1.Institute for Geodesy and GeoinformationUniversity of BonnBonnGermany
  2. 2.Department of Mechanical EngineeringMassachusetts Institute of TechnologyCambridgeUSA
  3. 3.Udacity Inc.Mountain ViewUSA

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