Abstract
The Iterative Closest Point (ICP) algorithm has been viewed as a standard approach to registering two point clouds. In the process of point clouds registration, the eliminating incorrect point pairs has important effect on the accuracy and stability of registration. In the past two decades, numerous strategies of excluding point pairs have been developed and various combinations of them have been applied to the variants of ICP algorithm. In this paper, an efficient combination of rejection strategies is proposed. It also is compared with other heuristic combinations. As shown in our case studies, the proposed combination can realize more accurate registration without sacrificing computational efficiency.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Besl, P.J., McKay, N.D.: Method for registration of 3-D shapes. IEEE Trans. Pattern Anal. Mach. Intell. 14(2), 239–256 (1992)
Serafin, J., Grisetti, G.: NICP: dense normal based point cloud registration. In: 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 742–749. IEEE Press, Hamburg (2015)
Chen, Y., Medioni, G.: Object modelling by registration of multiple range images. Image Vis. Comput. 10(3), 145–155 (1992)
Censi, A.: An ICP variant using a point-to-line metric. In: 2008 IEEE International Conference on Robotics and Automation, pp. 19–25. IEEE Press (2008)
Chetverikov, D., Svirko, D., Stepanov, D., Krsek, P.: The trimmed iterative closest point algorithm. In: 16th International Conference on Pattern Recognition, pp. 545–548. IEEE Press (2002)
May, S., Droeschel, D., Holz, D., Fuchs, S., Malis, E., Nüchter, A., Hertzberg, J.: Three-dimensional mapping with time-of-flight cameras. JFR 26(11–12), 934–965 (2009)
Pulli, K.: Multiview registration for large data sets. In: The Second International Conference on 3-D Digital Imaging and Modeling, pp. 160–168. IEEE Press (1999)
Pomerleau, F., Colas, F., Ferland, F., Michaud, F.: FSR, pp. 229–238. Springer, Heidelberg (2010). doi:10.1007/978-3-642-13408-1_21
Pajdla, T., Van Gool, L.: Matching of 3-D curves using semi-differential invariants. In: The Fifth International Conference on Computer Vision, pp. 390–395. IEEE Press (1995). doi:10.1109/iccv.1995.466913
Rusinkiewicz, S., Levoy, M.: Efficient variants of the ICP algorithm. In: The Third International Conference on 3-D Digital Imaging and Modeling, pp. 145–152. IEEE Press (2001). doi:10.1109/im.2001.924423
Zinßer, T., Schmidt, J., Niemann, H.: A refined ICP algorithm for robust 3-D correspondence estimation. In: 2003 International Conference on Image Processing, vol. 2, pp. II-695. IEEE Press (2003). doi:10.1109/icip.2003.1246775
Armesto, L., Minguez, J., Montesano, L.: A generalization of the metric-based iterative closest point technique for 3D scan matching. In: 2010 IEEE International Conference on Robotics and Automation, pp. 1367–1372. IEEE Press (2010). doi:10.1109/robot.2010.5509371
Nuchter, A., Lingemann, K., Hertzberg, J.: Cached KD tree search for ICP algorithms. In: Sixth International Conference on 3-D Digital Imaging and Modeling, pp. 419–426. IEEE Press (2007). doi:10.1109/3dim.2007.15
Arun, K.S., Huang, T.S., Blostein, S.D.: Least-squares fitting of two 3-D point sets. IEEE Trans. Pattern Anal. Mach. Intell. 5, 698–700 (1987). doi:10.1109/cdc.1997.649861
Hornung, A., Wurm, K.M., Bennewitz, M., Stachniss, C., Burgard, W.: OctoMap: an efficient probabilistic 3D mapping framework based on octrees. Auton. Robots 34(3), 189–206 (2013). doi:10.1007/s10514-012-9321-0
Acknowledgement
The authors greatly acknowledge the grant support from the Fundamental Research Funds for the Central Universities under contract number ZYGX2015J082.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Zhao, S., Zuo, L., Zhang, CH., Liu, Y. (2017). Efficient Combinations of Rejection Strategies for Dense Point Clouds Registration. In: Huang, Y., Wu, H., Liu, H., Yin, Z. (eds) Intelligent Robotics and Applications. ICIRA 2017. Lecture Notes in Computer Science(), vol 10463. Springer, Cham. https://doi.org/10.1007/978-3-319-65292-4_54
Download citation
DOI: https://doi.org/10.1007/978-3-319-65292-4_54
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-65291-7
Online ISBN: 978-3-319-65292-4
eBook Packages: Computer ScienceComputer Science (R0)