World Modeling

  • Wolfram BurgardEmail author
  • Martial Hebert
  • Maren Bennewitz
Part of the Springer Handbooks book series (SHB)


In this chapter we describe popular ways to represent the environment of a mobile robot. For indoor environments, which are often stored using two-dimensional representations, we discuss occupancy grids, line maps, topological maps, and landmark-based representations. Each of these techniques has its own advantages and disadvantages. Whilst occupancy grid maps allow for quick access and can efficiently be updated, line maps are more compact. Also landmark-based maps can efficiently be updated and maintained, however, they do not readily support navigation tasks such as path planning like topological representations do.

Additionally, we discuss approaches suited for outdoor terrain modeling. In outdoor environments, the flat-surface assumption underling many mapping techniques for indoor environments is no longer valid. A very popular approach in this context are elevation and variants maps, which store the surface of the terrain over a regularly spaced grid. Alternatives to such maps are point clouds, meshes, or three-dimensional grids, which provide a greater flexibility but have higher storage demands.


Point Cloud Mobile Robot Path Planning Indoor Environment Extended Kalman Filter 
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.







extended Kalman filter


expectation maximization


simultaneous localization and mapping


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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Wolfram Burgard
    • 1
    Email author
  • Martial Hebert
    • 2
  • Maren Bennewitz
    • 3
  1. 1.Institute of Computer ScienceUniversity of FreiburgFreiburgGermany
  2. 2.The Robotics InstituteCarnegie Mellon UniversityPittsburghUSA
  3. 3.Institute for Computer Science VIUniversity of BonnBonnGermany

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