A Probabilistic Approach to Concurrent Mapping and Localization for Mobile Robots

Sebastian Thrun, Dieter Fox, and Wolfram Burgard

This paper addresses the problem of building large-scale geometric maps of indoor environments with mobile robots. It poses the map building problem as a constrained, probabilistic maximum-likelihood estimation problem. It then devises a practical algorithm for generating the most likely map from data, along with the most likely path taken by the robot. Experimental results in cyclic environments of size up to 80 by 25 meter illustrate the appropriateness of the approach.

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@ARTICLE{Thrun98c,
  AUTHOR         = {S. Thrun and D. Fox and W. Burgard},
  YEAR           = {1998},
  TITLE          = {A Probabilistic Approach to Concurrent Mapping and 
                   Localization for Mobile Robots},
  JOURNAL        = {Machine Learning},
  VOLUME         = {31},
  PAGES          = {29--53},
  NOTE           = {also appeared in Autonomous Robots 5, 253--271 
                   (joint issue)}
}