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Autonomous Robots

, Volume 37, Issue 3, pp 243–260 | Cite as

Feature based graph-SLAM in structured environments

  • P. de la PuenteEmail author
  • D. Rodriguez-Losada
Article

Abstract

Introducing a priori knowledge about the latent structure of the environment in simultaneous localization and mapping (SLAM), can improve the quality and consistency results of its solutions. In this paper we describe and analyze a general framework for the detection, evaluation, incorporation and removal of structure constraints into a feature-based graph formulation of SLAM. We specifically show how including different kinds and levels of features in a hierarchical manner allows the system to easily discover new structure and why it makes more sense than other possible representations. The main algorithm in this framework follows an expectation maximization approach to iteratively infer the most probable structure and the most probable map. Experimental results show how this approach is suitable for the integration of a large variety of constraints and how our method can produce nice and consistent maps in regular environments.

Keywords

Mobile robots Mapping SLAM  Structured environments 

Notes

Acknowledgments

The authors would like to thank Mike Bosse for collecting and sharing the Killian Court data set. Thanks go to Dieter Fox for providing the Intel Research Lab data. This data set was obtained from the Robotics Data Set Repository (Radish) (Howard and Roy 2003). This work was supported in part by Comunidad de Madrid and European Social Fund (ESF) programs and also by the Spanish Ministry of Science and Technology, DPI2010-21247-C02-01—ARABOT (Autonomous Rational Robots).

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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.ETSI IndustrialesUniversidad Politecnica de MadridMadridSpain

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