Abstract
The iterative closest point (ICP) algorithm is used to fine tune the alignment of two point clouds in many pose estimation algorithms. The uncertainty in these pose estimation algorithms is thus mainly dependent on the pose uncertainty in ICP.
This paper investigates the uncertainties in the ICP algorithm by the use of Monte Carlo simulation. A new descriptor based on object shape and a pose error descriptor are introduced. Results show that it is reasonable to approximate the pose errors by multivariate Gaussian distributions, and that there is a linear relationship between the parameters of the Gaussian distributions and the shape descriptor. As a consequence the shape descriptor potentially provides a computationally cheap way to approximate pose uncertainties.
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Notes
- 1.
It is possible to construct point clouds where some of the principal axes are degenerate, such as for a sphere or cylinder. If the degeneracy is due to rotational symmetry in the object, arbitrary axes can be chosen, since the corresponding pose error will display the same symmetry. Other causes for the degeneracy are not investigated in this paper.
- 2.
As the choice of positive direction of both principal axes and error axes are arbitrary, they are always chosen so the angle between them is \(0\le angle \le \pi /2\).
- 3.
The base implementation in Point Cloud Library 1.7 is a modified version of the original ICP algorithm. See the Point Cloud Library documentation for details: http://docs.pointclouds.org/1.7.0/classpcl_1_1_iterative_closest_point.html.
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Acknowledgements
The research leading to these results has been funded in part by Innovation Fund Denmark as a part of the project “MADE - Platform for Future Production”.
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Iversen, T.M., Buch, A.G., Krüger, N., Kraft, D. (2015). Shape Dependency of ICP Pose Uncertainties in the Context of Pose Estimation Systems. In: Nalpantidis, L., Krüger, V., Eklundh, JO., Gasteratos, A. (eds) Computer Vision Systems. ICVS 2015. Lecture Notes in Computer Science(), vol 9163. Springer, Cham. https://doi.org/10.1007/978-3-319-20904-3_28
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