Abstract
In this study, a new automatic point cloud registration algorithm based on point cloud registration is proposed to broaden registration ways. The proposed method extracts features of point cloud region for performing the coarse registration. Based on the coarse registration results, the Iterative Closest Point (ICP) algorithm is used for performing the fine registration to restore the measured model. The proposed registration approach is able to do automatic registration without any assumptions about initial positions, and avoid the problems of traditional ICP algorithm in the bad initial estimation. The proposed method along with ICP algorithm provides efficient 3D modeling for computer-aided engineering, computer-aided design and application with Kinect.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Gelfand, N., Ikemoto, L., Rusinkiewicz, S., Levoy, M.: Geometrically stable sampling for the ICP algorithm. In: Proc. 2003 The Fourth International Conference on 3D Digital Imaging and Modeling (3DIM 2003), pp. 260–267 (2003)
Besl, P.J., McKay, N.D.: A method for registration of 3-D shapes. IEEE Transaction on Pattern Analysis and Machine Intelligence 14, 239–256 (1992)
Newcombe, R.A., Izadi, S., Hilliges, O., Molyneaux, D., Kim, D., Davison, A.J., Kohli, P., Shotton, J., Hodges, S., Fitzgibbon, A.: KinectFusion: real-time dense surface mapping and tracking. In: Proc.2011 IEEE 10th International Symposium on Mixed and Augmented Reality (ISMAR), pp. 127–136 (2011)
Schindhelm, C.K.: Evaluating SLAM approaches for Microsoft Kinect. In: Proc. 2011 The Eighth International Conference on Wireless and Mobile Communications (ICWMC 2012), Venice, pp. 402–407 (2012)
Nüchter, A., Surmann, H., Hertzberg, J.: Automatic model refinement for 3D reconstruction with mobile robots. In: Proc. 2003 The Fourth International Conference on 3-D Digital Imaging and Modeling (3DIM 2003), pp. 394–401 (2003)
Henry, P., Krainin, M., Herbst, E., Ren, X., Fox, D.: RGB-D mapping: using Kinect-style depth cameras for dense 3D modeling of indoor environments. The International Journal of Robotics Research 31, 647–663 (2012)
Guo, W., Du, T., Zhu, X., Hu, T.: Kinect-based real-time RGB-D image fusion method. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXIX-B3:XXII, pp. 275–279 (2012)
Lowe, D.G.: Object recognition from local scale-invariant features. In: Proc. 1999 International Conference on Computer Vision, Corfu, pp. 1150–1157 (1999)
Xu, F., Zhao, X., Hagiwara, I.: A Study on automatic registration in reverse engineering. Transactions of the Japan Society of Mechanical Engineers C76, 2861–2869 (2010)
Xu, F., Zhao, X., Hagiwara, I.: Research on high-speed automatic registration using Composite-Descriptor-Could-Points (CDCP) Model. Transactions of the Japan Society of Mechanical Engineers A78, 783–789 (2012)
Curtis, P., Payeur, P.: A method to segment a 3D surface point cloud for selective sensing in robotic exploration. In: Proc. 2010 IEEE International Workshop on Robotic and Sensors Environments (ROSE), pp. 1–6 (2010)
Kenny, J.F.: Root Mean Square. In: Mathematics of Statistics, Pt.1, 3rd edn., pp. 59–60. Van Nostrand, Princeton (1962)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Liao, Y., Xu, F., Zhao, X., Hagiwara, I. (2014). A Point Cloud Registration Method Based on Point Cloud Region and Application Samples. In: Tanaka, S., Hasegawa, K., Xu, R., Sakamoto, N., Turner, S.J. (eds) AsiaSim 2014. AsiaSim 2014. Communications in Computer and Information Science, vol 474. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45289-9_19
Download citation
DOI: https://doi.org/10.1007/978-3-662-45289-9_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-45288-2
Online ISBN: 978-3-662-45289-9
eBook Packages: Computer ScienceComputer Science (R0)