Total Least Squares Registration of 3D Surfaces

Year 2015, Volume: 2 Issue: 2, 27 - 38, 03.08.2015

Umut Aydar M. Orham Altan

https://doi.org/10.30897/ijegeo.303539

Cited By: 2

Abstract

Co-registration of point clouds of partially scanned
objects is the first step of the 3D modeling workflow. The aim of
co-registration is to merge the overlapping point clouds by estimating the
spatial transformation parameters. In computer vision and photogrammetry domain
one of the most popular methods is the ICP (Iterative Closest Point) algorithm
and its variants. There exist the 3D Least Squares (LS) matching methods as
well (Gruen and Akca, 2005). The
co-registration methods commonly use the least squares (LS) estimation method
in which the unknown transformation parameters of the (floating) search surface
is functionally related to the observation of the (fixed) template surface.
Here, the stochastic properties of the search surfaces are usually omitted.
This omission is expected to be minor and does not disturb the solution vector
significantly. However, the a posteriori covariance matrix will be affected by
the neglected uncertainty of the function values of the search surface. . This
causes deterioration in the realistic precision estimates. In order to overcome
this limitation, we propose a method where the stochastic properties of both
the observations and the parameters are considered under an errors-in-variables
(EIV) model. The experiments have been carried out using diverse laser scanning
data sets and the results of EIV with the ICP and the conventional LS matching
methods have been compared.

Keywords

Laser scanning, Point Cloud, Registration, Matching

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