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A Probabilistic Approach to Concurrent Mapping and Localization for Mobile Robots

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Abstract

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 80×25 m illustrate the appropriateness of the approach.

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Authors and Affiliations

  1. Computer Science Department and Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, 15213-3891

    Sebastian Thrun

  2. Institut für Informatik III, Universität Bonn, D-53117, Bonn, Germany

    Wolfram Burgard & Dieter Fox

Authors
  1. Sebastian Thrun
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  2. Wolfram Burgard
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  3. Dieter Fox
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Thrun, S., Burgard, W. & Fox, D. A Probabilistic Approach to Concurrent Mapping and Localization for Mobile Robots. Autonomous Robots 5, 253–271 (1998). https://doi.org/10.1023/A:1008806205438

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