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.
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 subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Besl, P.J., Mckay, N.D.: A method for registration of 3-D shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence 14(2), 239–256 (1992)
Calderón, F., Ruiz, U., Rivera, M.: Surface-normal estimation with neighborhood reorganization for 3d reconstruction. In: CIARP, pp. 321–330 (2007)
Chen, Y., Medioni, G.: Object modelling by registration of multiple range images. Image Vision Comput. 10(3), 145–155 (1992)
Chua, C.S., Jarvis, R.: 3D free-form surface registration and object recognition. International Journal of computer vision (1996)
Diosi, A., Kleeman, L.: Fast laser scan matching in polar coordinates. Journal of Robotics Research, 1125–1153 (2007)
Dudek, G., Jenkin, M.: Computational principles of mobile robotics. Cambridge University Press, New York (2000)
Freidman, J.H., Bentley, J.L., Finkel, R.A.: An algorithm for finding best matches in logarithmic expected time. ACM Trans. Math. Softw. 3(3), 209–226 (1977)
Lu, F., Milios, E.: Robot pose estimation in unknown environments by matching 2d range scans. Journal of Intelligent and Robotic Systems 18, 935–938 (1997)
Minguez, J., Montano, L., Lamiraux, F.: Metric–based iterative closest point scan matching for sensor displacement estimation. IEEE Transactions on Robotics 22(5), 1047–1054 (2006)
Nocedal, J., Wright, S.J.: Numerical Optimization. Springer, Heidelberg (1999)
Pulli, K.: Multiview registration for large data sets. In: Third International Conference on 3D Digital Imaging and Modeling (3DIM), p. 0160 (1999)
Rusinkiewicz, S., Levoy, M.: Efficient variants of the ICP algorithm. In: Third International Conference on 3D Digital Imaging and Modeling (3DIM) (June 2001)
Sharp, G., Lee, S., Wehe, D.: Icp registration using invariant features. IEEE Trans. on PAMI 24(1), 90–102 (2002)
Zhang, Z.: Iterative point matching for registration of free-form curves and surfaces. Int. J. Comput. Vision 13(2), 119–152 (1994)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lara, C., Romero, L., Calderón, F. (2008). A Robust Iterative Closest Point Algorithm with Augmented Features. In: Gelbukh, A., Morales, E.F. (eds) MICAI 2008: Advances in Artificial Intelligence. MICAI 2008. Lecture Notes in Computer Science(), vol 5317. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88636-5_58
Download citation
DOI: https://doi.org/10.1007/978-3-540-88636-5_58
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88635-8
Online ISBN: 978-3-540-88636-5
eBook Packages: Computer ScienceComputer Science (R0)