Sparse Iterative Closest Point

Rigid registration of two geometric data sets is essential in many applications, including robot navigation, surface reconstruction, and shape matching. Most commonly, variants of the Iterative Closest Point (ICP) algorithm are employed for this task. These methods alternate between closest point co...

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Veröffentlicht in:Computer graphics forum Jg. 32; H. 5; S. 113 - 123
Hauptverfasser: Bouaziz, Sofien, Tagliasacchi, Andrea, Pauly, Mark
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Oxford, UK Blackwell Publishing Ltd 01.08.2013
Schlagworte:
Algorithms
and systems
Computer graphics
Computer science
Heuristic
I.3.5 [Computer Graphics]: Computational Geometry and Object Modeling-Geometric algorithms
I.3.5 [Computer Graphics]: Computational Geometry and Object Modeling—Geometric algorithms, languages, and systems
Iterative methods
languages
Optimization
Registration
Robots
Studies
Three dimensional
Three dimensional models
United States > US
ISSN:0167-7055, 1467-8659
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Zusammenfassung:Rigid registration of two geometric data sets is essential in many applications, including robot navigation, surface reconstruction, and shape matching. Most commonly, variants of the Iterative Closest Point (ICP) algorithm are employed for this task. These methods alternate between closest point computations to establish correspondences between two data sets, and solving for the optimal transformation that brings these correspondences into alignment. A major difficulty for this approach is the sensitivity to outliers and missing data often observed in 3D scans. Most practical implementations of the ICP algorithm address this issue with a number of heuristics to prune or reweight correspondences. However, these heuristics can be unreliable and difficult to tune, which often requires substantial manual assistance. We propose a new formulation of the ICP algorithm that avoids these difficulties by formulating the registration optimization using sparsity inducing norms. Our new algorithm retains the simple structure of the ICP algorithm, while achieving superior registration results when dealing with outliers and incomplete data. The complete source code of our implementation is provided at http://lgg.epfl.ch/sparseicp.
Bibliographie:ArticleID:CGF12178
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ISSN:0167-7055
1467-8659
DOI:10.1111/cgf.12178