A Robust Iterative Closest Point Algorithm with Augmented Features

Authors:
Carlos Lara;Leonardo Romero;Félix Calderón
Affiliations:
División de Estudios de Posgrado, Facultad de Ingeniería Eléctrica, Universidad Michoacana de San Nicolás de Hidalgo, Morelia, México CP 58000;División de Estudios de Posgrado, Facultad de Ingeniería Eléctrica, Universidad Michoacana de San Nicolás de Hidalgo, Morelia, México CP 58000;División de Estudios de Posgrado, Facultad de Ingeniería Eléctrica, Universidad Michoacana de San Nicolás de Hidalgo, Morelia, México CP 58000
Venue:
MICAI '08 Proceedings of the 7th Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence
Year:
2008

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Abstract

The Iterative Closest Point (ICP) is widely used for 2D - 3D alignment when an initial estimate of the relative pose is known. Many ICP variants have been proposed, affecting all phases of the algorithm from the point selection and matching to the minimization strategy.This paper presents a method for 2D laser scan matching that modifies the matching phase. In the first stage of the matching phase our method follows the ordinary association strategy: for each point of the new-scan it finds the closest point in the reference-scan. In a second stage, the most probable normal vector difference is calculated and associations that do not fulfill the normal vector difference requirement are re-associated by finding a better association in the neighborhood. This matching strategy improves the ICP performance specially when the initial estimate is not close to the right one, as it is shown in the simulated and real tests.