Simultaneous Localization and Mapping

  • Cyrill Stachniss
  • John J. Leonard
  • Sebastian Thrun

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 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.

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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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Cyrill Stachniss
    • 1
  • 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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