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A Genetic Algorithm Approach for the Rigorous Registration of Arbitrary Laser Scanner Point Clouds

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Computational Engineering

Abstract

Terrestrial laser scanners have achieved great popularity in the last decade. Their easy on-site application and the possibility of flexible and high quality post processing added to their success in several fields such as architectural, archaeological, and heritage documentation. We present a method for handling the automatic registration of point clouds which are characterized by significant noise level, generally imperfect geometry and occlusions. Hereby we combine and extend already existing and established methods to facilitate the registration of point clouds without prior pre-processing. Our approach consists—similar to other methods—of three steps which are scan analysis, pair-wise registration, and global registration. To handle the abovementioned datasets we propose to use imperfect and subdivided features, and to implement Genetic Algorithms (GAs). At the same time our approach can be seen as extension to already known Genetic Algorithms used for the registration of point clouds. By implementing an adapted version of a Genetic Algorithm in the classical registration process between rough alignment and fine registration, we are able to maintain robustness and computational performance also when registering point clouds of bigger objects characterized by a notably increased number of points, a significant noise level, and occlusions. We show and discuss the successful application of the algorithm on a scene which does not consist of classical geometric primitives such as planes.

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Acknowledgements

The dataset discussed in this article was provided by SFB HiMAT, the Special Research Program on the HiMAT. The project was mainly funded by the Austrian Science Fund (FWF), TransIDEE (the Science and Technology Transfer Center of the University of Innsbruck), the regional authorities of Tyrol, Salzburg, and Vorarlberg as well as the Autonomous Province Bolzano/South-Tyrol (Italy).

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  1. Institute for Basic Sciences in Engineering, Unit of Surveying and Geoinformation, University of Innsbruck, Technikerstr. 13, A6020, Innsbruck, Austria

    K. Hanke & S. Schenk

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  1. K. Hanke
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Correspondence to K. Hanke .

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  1. Institute of Basic Sciences in Engineering Science, University of Innsbruck, Innsbruck, Austria

    Günter Hofstetter

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Hanke, K., Schenk, S. (2014). A Genetic Algorithm Approach for the Rigorous Registration of Arbitrary Laser Scanner Point Clouds. In: Hofstetter, G. (eds) Computational Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-05933-4_9

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