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Improving 3D Keypoint Detection from Noisy Data Using Growing Neural Gas

  • José Garcia-Rodriguez
  • Miguel Cazorla
  • Sergio Orts-Escolano
  • Vicente Morell
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7903)

Abstract

3D sensors provides valuable information for mobile robotic tasks like scene classification or object recognition, but these sensors often produce noisy data that makes impossible applying classical keypoint detection and feature extraction techniques. Therefore, noise removal and downsampling have become essential steps in 3D data processing. In this work, we propose the use of a 3D filtering and down-sampling technique based on a Growing Neural Gas (GNG) network. GNG method is able to deal with outliers presents in the input data. These features allows to represent 3D spaces, obtaining an induced Delaunay Triangulation of the input space. Experiments show how the state-of-the-art keypoint detectors improve their performance using GNG output representation as input data. Descriptors extracted on improved keypoints perform better matching in robotics applications as 3D scene registration.

Keywords

GNG Noisy Point Cloud Visual Features Keypoint Detection Filtering 3D Scene Registration 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • José Garcia-Rodriguez
    • 1
  • Miguel Cazorla
    • 2
  • Sergio Orts-Escolano
    • 1
  • Vicente Morell
    • 2
  1. 1.Department of Computing TechnologyUniversity of AlicanteSpain
  2. 2.Instituto de Investigación en InformáticaUniversity of AlicanteSpain

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