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Kinect Enabled Monte Carlo Localisation for a Robotic Wheelchair

  • Theodoros Theodoridis
  • Huosheng Hu
  • Klaus McDonald-Maier
  • Dongbing Gu
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 193)

Abstract

Proximity sensors and 2D vision methods have shown to work robustly in particle filter-based Monte Carlo Localisation (MCL). It would be interesting however to examine whether modern 3D vision sensors would be equally efficient for localising a robotic wheelchair with MCL. In this work, we introduce a visual Region Locator Descriptor, acquired from a 3D map using the Kinect sensor to conduct localisation. The descriptor segments the Kinect’s depth map into a grid of 36 regions, where the depth of each column-cell is being used as a distance range for the measurement model of a particle filter. The experimental work concentrated on a comparison of three different localization cases. (a) an odometry model without MCL, (b) with MCL and sonar sensors only, (c) with MCL and the Kinect sensor only. The comparative study demonstrated the efficiency of a modern 3D depth sensor, such as the Kinect, which can be used reliably for wheelchair localisation.

Keywords

Monte Carlo Localisation Particle Filter Localisation Region Locator Descriptors Kinect sensor 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Theodoros Theodoridis
    • 1
  • Huosheng Hu
    • 1
  • Klaus McDonald-Maier
    • 1
  • Dongbing Gu
    • 1
  1. 1.School of Computer Science and Electronic EngineeringUniversity of EssexColchesterUK

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