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.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 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.
References
Bengtsson, O., Baerveldt, A.J.: Robot localization based on scan-matching - estimating the covariance matrix for the IDC algorithm. Robot. Auton. Syst. 44(1), 29–40 (2003)
Besl, P., McKay, N.D.: A method for registration of 3-D shapes. IEEE Trans. Pattern Anal. Mach. Intell. 14(2), 239–256 (1992)
Brujic, D., Ristic, M.: Monte carlo simulation and analysis of free-form surface registration. Proc. Inst. Mech. Eng. Part B: J. Eng. Manuf. 211(8), 605–617 (1997)
Buch, J.P., Laursen, J.S., Sørensen, L.C., Ellekilde, L.-P., Kraft, D., Schultz, U.P., Petersen, H.G.: Applying simulation and a domain-specific language for an adaptive action library. In: Brugali, D., Broenink, J.F., Kroeger, T., MacDonald, B.A. (eds.) SIMPAR 2014. LNCS, vol. 8810, pp. 86–97. Springer, Heidelberg (2014)
Censi, A.: An accurate closed-form estimate of ICP’s covariance. In: 2007 IEEE International Conference on Robotics and Automation, pp. 3167–3172, April 2007
Christensen, H.I., et al.: A roadmap for u.s. robotics: from internet to robotics. Computing Community Consortium and Computing Research Association, Washington, DC (US) (2013)
Fitzgibbon, A.W.: Robust registration of 2D and 3D point sets. Image Vis. Comput. 21(13), 1145–1153 (2003)
Gelfand, N., Ikemoto, L., Rusinkiewicz, S., Levoy, M.: Geometrically stable sampling for the ICP algorithm. In: Proceedings of the Fourth International Conference on 3-D Digital Imaging and Modeling, 3DIM 2003, pp. 260–267 (2003)
Glover, J., Popovic, S.: Bingham procrustean alignment for object detection in clutter. In: 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2158–2165 (2013)
Guo, Y., Bennamoun, M., Sohel, F., Lu, M., Wan, J.: 3D object recognition in cluttered scenes with local surface features: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 36(11), 2270–2287 (2014)
Kasper, A., Xue, Z., Dillmann, R.: The kit object models database: an object model database for object recognition, localization and manipulation in service robotics. Int. J. Robot. Res. (IJRR) 31(8), 927–934 (2012)
Mian, A.S., Bennamoun, M., Owens, R.: Three-dimensional model-based object recognition and segmentation in cluttered scenes. IEEE Trans. Pattern Anal. Mach. Intell. 28(10), 1584–1601 (2006)
Stoddart, A.J., Lemke, S., Hilton, A., Renn, T.: Estimating pose uncertainty for surface registration. Image Vis. Comput. 16(2), 111–120 (1998)
Zinßer, T., Schmidt, J., Niemann, H.: A refined ICP algorithm for robust 3-D correspondence estimation. In: Proceedings of the 2003 International Conference on Image Processing, ICIP 2003, vol. 2, p. II-695 (2003)
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”.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-20904-3_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-20903-6
Online ISBN: 978-3-319-20904-3
eBook Packages: Computer ScienceComputer Science (R0)