Efficient Multi-hypotheses Unscented Kalman Filtering for Robust Localization

  • Gregor Jochmann
  • Sören Kerner
  • Stefan Tasse
  • Oliver Urbann
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7416)


This paper describes an approach to Gaussian mixture filtering which combines the accuracy of the Kalman filter and the robustness of particle filters without sacrificing computational efficiency. Critical approximations of common Gaussian mixture algorithms are analyzed and similarities are pointed out to particle filtering with an extremely low number of particles. Known techniques from both fields are applied in a new combination resulting in a multi-hypotheses Kalman filter which is superior to common Kalman filters in its ability of fast relocalization in kidnapped robot scenarios and its representation of multi-modal belief distributions, and which outperforms particle filters in localization accuracy and computational efficiency.


Mobile Robot Gaussian Mixture Model Humanoid Robot Sensor Model Goal Post 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Gregor Jochmann
    • 1
  • Sören Kerner
    • 1
  • Stefan Tasse
    • 1
  • Oliver Urbann
    • 1
  1. 1.Robotics Research Institute, Section Information TechnologyTU Dortmund UniversityDortmundGermany

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