A New Feature Detection Algorithm Based on RANSAC

Yuan Jiang Huang; Jie Huang

doi:10.4028/www.scientific.net/AMR.971-973.1477

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 971-973A New Feature Detection Algorithm Based on RANSAC

A New Feature Detection Algorithm Based on RANSAC

Abstract:

A improved RANSAC algorithm was introduced into the segmentation of LiDAR and r-radius point density was put forward to the estimation criterion,which aims to remove the discrete point outside the feature plane.an accurate registration is achieved by improving RANSAC algorithim after an analysis on the advantages and disadvantages of the algorithm for objects with many planar feature.The algorithm are implemented with VC++ and VTK platform,tested by real data collected on the test area,it verify the effectiveness and accuracy of the proposed algorithms.

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Periodical:

Advanced Materials Research (Volumes 971-973)

Pages:

1477-1480

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.971-973.1477

Citation:

Cite this paper

Online since:

June 2014

Authors:

Yuan Jiang Huang*, Jie Huang

Keywords:

Detection Algorithm, Point Cloud Segmentation, RANSAC, Registration

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* - Corresponding Author

References

[2] Li H, Li D R, Huang X F and Zhong C. 2009. Advanced adaptive TIN filter for LIDAR point clouddata. Science of Surveying and Map-ping, 34(3): 39–40.

Google Scholar

[3] Liu J N and Zhang X H. 2005. Classification of laser scanning altimetry data using laser intensity. Geomatics and Information Science of Wuhan University, 30(3): 189–193.

Google Scholar

[4] Lohmann P, Koch A and Schaeffer M. 2000. Approaches to the filtering of laser scanner data. International Archives of Photogrammetryand Remote Sensing, 33(B3): 540–547.

Google Scholar

[5] Wang O, Lodha S K and Helmbold D P. 2006. A bayesian approach to building footprint extraction from aerial lidar data. Proceedings of the International Symposium on 3D Data Processing, Visualization and Transmission 1-16 June Chapel Hill USA: 192–199.

DOI: 10.1109/3dpvt.2006.9

Google Scholar

[6] Raber G, Jensen J R, Hodgson M E, Tullis J A, Davis B A and Berglund J. 2007. Impact of LiDAR nominal posting density on DEM accuracy and flood zone delineation. Photogrammetric Engineering&Remote Sensing, 73(7): 793–804.

DOI: 10.14358/pers.73.7.793

Google Scholar

[7] Filin, S. Preife, N. Segmentation of airborne laser scanning data using a slope adaptive neighborhood [J]. ISPRS Journal of Photogrammetry and Remote Sensing: 2006, 60(2): 71-80.

DOI: 10.1016/j.isprsjprs.2005.10.005

Google Scholar