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Model-Based Visual Self-localization Using Gaussian Spheres

  • David Gonzalez-AguirreEmail author
  • Tamim Asfour
  • Eduardo Bayro-Corrochano
  • Ruediger Dillmann
Chapter

Abstract

A novel model-based approach for global self-localization using active stereo vision and density Gaussian spheres is presented. The proposed object recognition components deliver noisy percept subgraphs, which are filtered and fused into an ego-centered reference frame. In subsequent stages, the required vision-to-model associations are extracted by selecting ego-percept subsets in order to prune and match the corresponding world-model subgraph. Ideally, these coupled subgraphs hold necessary information to obtain the model-to-world transformation, i.e., the pose of the robot. However, the estimation of the pose is not robust due to the uncertainties introduced when recovering Euclidean metric from images and during the mapping from the camera to the ego-center. The approach models the uncertainty of the percepts with a radial normal distribution. This formulation allows a closed-form solution which not only derives the maximal density position depicting the optimal ego-center but also ensures the solution even in situations where pure geometric spheres might not intersect.

Keywords

Humanoid Robot Kinematic Chain Geometric Algebra Point Pair World Model 
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 London 2010

Authors and Affiliations

  • David Gonzalez-Aguirre
    • 1
    Email author
  • Tamim Asfour
    • 1
  • Eduardo Bayro-Corrochano
    • 2
  • Ruediger Dillmann
    • 3
  1. 1.Humanoids and Intelligence Systems LabKarlsruhe Institute of TechnologyKarlsruheGermany
  2. 2.Computer Science, CINVESTAV Unidad GuadalajaraCentro de Investigación y Estudios Avanzados del I.P.N (Cinvestav)ZapopanMexico
  3. 3.Karlsruhe Institute of TechnologyKarlsruheGermany

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