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Robust Estimation

  • Joseph L. Awange
  • Béla Paláncz
Chapter

Abstract

In many fields of geosciences such as robotics [413], computer vision [351], digital photogrammetry [538], surface reconstruction [388], computational geometry [336], digital building modelling [48], forest planning and operational activities [386] to list but a few, it is a fundamental task to extract plane features from three-dimensional (3D) point clouds, – i.e., a vast amount of points reflected from the surface of objects collected – using the cutting edge remote sensing technology of laser scanning, e.g., [450]. Due to the physical limitations of the sensors, occlusions, multiple reflectance, and noise can produce off-surface points, thereby necessitating robust fitting techniques. Robust fitting means an estimation technique, which is able to estimate accurate model parameters not only consisting of small error magnitudes in the data set but occasionally large scale measurement errors (outliers). Outliers definition is not easy. Perhaps considering the problem from a practical point of the view, one can say that data points, whose appearance in the data set causes dramatically change in the result of the estimated parameters can be labeled as outliers. Basically, there are two different methods to handle outliers;
  1. (i)

    weighting out outliers

     
  2. (ii)

    discarding outliers

     

Keywords

Principal Component Analysis Point Cloud Ordinary Little Square Singular Value Decomposition Point Cloud Data 
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 2016

Authors and Affiliations

  • Joseph L. Awange
    • 1
  • Béla Paláncz
    • 2
  1. 1.Curtin UniversityPerthAustralia
  2. 2.Budapest University of Technology and EconomicsBudapestHungary

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