Localization and Mapping Corrections

  • Camillo Gentile
  • Nayef Alsindi
  • Ronald Raulefs
  • Carole Teolis


Chapter 9 gives an overview of localization and mapping with a focus on near real-time implementation. We look at sensors that provide information about the environment (allothetic) and that aid us in creating a map of what is around us. The created map is also used for localization. This classic problem of simultaneous localization and mapping (SLAM) requires fusion of information from idiothetic and allothetic sensors. The basic idea of SLAM is that if the sensor and algorithms can identify a landmark and a location of that landmark relative to tracked subject, then any time that landmark is seen again, its location can be used to correct the tracked subject’s location. We discuss a small set of environmental sensors that can be used in SLAM algorithms including optical, magnetometer an inertial and discuss how features are selected. We give an overview of approaches to solving the SLAM problem and then show some results of a particular implementation.


SLAM: Simultaneous localization and mapping Idiothetic Allothetic Topological Metric Landmark/feature Optical feature Map inference Magnetic feature Bayes filter Markov assumption Gaussian Robust Kalman filter Extended Kalman filter Particle filter FAST-SLAM Factor graph HAR SLAM Correlation Global map Outlier removal 


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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Camillo Gentile
    • 1
  • Nayef Alsindi
    • 2
  • Ronald Raulefs
    • 3
  • Carole Teolis
    • 4
  1. 1.TechnologyNational Institute of Standards and TechnologyGaithersburgUSA
  2. 2.Etisalat BT Innovation Center (EBTIC)Khalifa University of Science, Technology and Research (KUSTAR)Abu DhabiUnited Arab Emirates (UAE)
  3. 3.German Aerospace CenterWesslingGermany
  4. 4.TRX SystemsGreenbeltUSA

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