An Improved Algorithm on Point Cloud Optimization for Unmanned Aerial Vehicles

Abstract:

It is hard to keep vertical photography for Unmanned Aerial Vehicles (UAV) and there is large deflection and rotation in the UAV image, which lead to accuracy of point cloud data produced by UAV image is not high. In this paper an algorithm on UAV point cloud optimization based on Patch-based Least Squares Image Matching is proposed. By optimizing the point cloud data with Patch-based Least Squares Image Matching, and test data with images in campus of Northwestern University. The experimental results show that UAV point cloud optimization based on Patch-based Least Squares Image Matching can conspicuously improve the accuracy of the UAV image matching.

• Periodical: Applied Mechanics and Materials (Volume 525)
• Pages: 752-758
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.525.752
• Online since: February 2014
• Authors: Nan Yang*, Zhen Feng Shao, Bing Xuan Guo, Zhe Peng, Lei Huang
• Keywords: Least Squares Matching, Point Cloud Optimization, Small Patch, Unmanned Aerial Vehicle (UAV)

References

[1] LIN Zongjian, LI Deren, XU Yanying. General review on the new progress of earth observations[J]. Science of Surveying and Mapping, 2011, 36(4): 5-8. (In Chinese).

[2] Seitz S M, Curless B, Diebel J, Scharstein D, Szeliski R. A comparison and evaluation of multi-view stereo reconstruction algorithms[C]. /Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on. IEEE, 2006, 1: 519-528.

DOI: 10.1109/cvpr.2006.19

[3] Calakli F, Ulusoy A O, Restrepo M I, et al. High Resolution Surface Reconstruction from Multi-view Aerial Imagery[C]/3D Imaging, Modeling, Processing, Visualization and Transmission (3DIMPVT), 2012 Second International Conference on. IEEE, 2012: 25-32.

DOI: 10.1109/3dimpvt.2012.54

[4] Furukawa Y, Ponce J. Accurate, Dense, and Robust Multiview Stereopsis[J]. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 2010, 32(8): 1362-1376.

DOI: 10.1109/tpami.2009.161

[5] Scharstein D, Szeliski R. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms[J]. International journal of computer vision, 2002, 47(1-3): 7-42.

DOI: 10.1109/smbv.2001.988771

[6] Liang T, Heipke C. Automatic relative orientation of aerial images[J]. Photogrammetric engineering and remote sensing, 1996, 62(1): 47-55.

[7] Hirschmuller H. Stereo vision in structured environments by consistent semi-global matching[C]/Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on. IEEE, 2006, 2: 2386-2393.

DOI: 10.1109/cvpr.2006.294

[8] Hirschmuller H. Stereo processing by semiglobal matching and mutual information[J]. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 2008, 30(2): 328-341.

DOI: 10.1109/tpami.2007.1166

[9] JIANG W S. Multiple Aerial Image Matching and Automatic Building Detection [D]. Wuhan: Wuhan University, 2004. (In Chinese ).

[10] ZHANG Guo, CHEN Tan, PAN Hongbo, JIANG Wanshou. Patch-based Least Squares Image Matching Based on Rational Polynomial Coefficients Model[J]. Acta Geodaetica et Cartographica Sinica, 2010, 39( 3): 264-270. (In Chinese).

[11] ACKERMANN F. High Precision Digital Image Correlation[C] / Proceeding of 39th Photogrammetry Week. Stuttgart: [s. n. ], 1983: 231-243.

[12] ROSENHOLM D. Multi-point Matching Using the Least Squares Technique for Evaluation of Three-dimensional Models[J]. Photogrammetric Engineering and Remote Sensing, 1987, 53(6): 621-626.

[13] BALTSAVIAS E P. Multiphoto Geometrically Constrained Matching[D]. Zurich: Institute of Geodesy and Photo-grammetry, ETH, 1991: 221-229.