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An Improved Evolutionary Approach for Egomotion Estimation with a 3D TOF Camera

  • Ivan Villaverde
  • Manuel Graña
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5602)

Abstract

We propose an evolutionary approach for egomotion estimation with a 3D TOF camera. It is composed of two main modules plus a preprocessing step. The first module computes the Neural Gas (NG) approximation of the preprocessed camera 3D data. The second module is an Evolution Strategy which performs the task of estimating the motion parameters by searching on the space of linear transformations restricted to the translation and rotation, applied on the codevector sets obtained by the NG for successive camera readings. The fitness function is the matching error between the transformed last set of codevectors and the codevector set corresponding to the next camera readings. In this paper, we report new modifications and improvements of this system and provide several comparisons between our and other well known registration algorithms.

Keywords

Point Cloud Registration Algorithm Matching Error Mobile Robotic Matching Distance 
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 2009

Authors and Affiliations

  • Ivan Villaverde
    • 1
  • Manuel Graña
    • 1
  1. 1.Computational Intelligence GroupUniversity of the Basque CountrySpain

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