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http://dx.doi.org/10.7848/ksgpc.2019.37.6.453

Feature-based Matching Algorithms for Registration between LiDAR Point Cloud Intensity Data Acquired from MMS and Image Data from UAV  

Choi, Yoonjo (School of Civil and Environmental Engineering, Yonsei University)
Farkoushi, Mohammad Gholami (School of Civil and Environmental Engineering, Yonsei University)
Hong, Seunghwan (Stryx Inc.)
Sohn, Hong-Gyoo (School of Civil and Environmental Engineering, Yonsei University)
Publication Information
Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography / v.37, no.6, 2019 , pp. 453-464 More about this Journal
Abstract
Recently, as the demand for 3D geospatial information increases, the importance of rapid and accurate data construction has increased. Although many studies have been conducted to register UAV (Unmanned Aerial Vehicle) imagery based on LiDAR (Light Detection and Ranging) data, which is capable of precise 3D data construction, studies using LiDAR data embedded in MMS (Mobile Mapping System) are insufficient. Therefore, this study compared and analyzed 9 matching algorithms based on feature points for registering reflectance image converted from LiDAR point cloud intensity data acquired from MMS with image data from UAV. Our results indicated that when the SIFT (Scale Invariant Feature Transform) algorithm was applied, it was able to stable secure a high matching accuracy, and it was confirmed that sufficient conjugate points were extracted even in various road environments. For the registration accuracy analysis, the SIFT algorithm was able to secure the accuracy at about 10 pixels except the case when the overlapping area is low and the same pattern is repeated. This is a reasonable result considering that the distortion of the UAV altitude is included at the time of UAV image capturing. Therefore, the results of this study are expected to be used as a basic research for 3D registration of LiDAR point cloud intensity data and UAV imagery.
Keywords
Mobile Mapping System; Unmanned Aerial Vehicle; Point Cloud Intensity Data; Reflectance Image; Feature Point Extraction;
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1 Hong, S., Park, I., Lee, J., Lim, K., Choi, Y., and Sohn, H.G. (2017), Utilization of a terrestrial laser scanner for the calibration of mobile mapping systems, Sensors, Vol. 17, No. 3, 24p.
2 Kim, T. and Im, Y.J. (2003), Automatic satellite image registration by combination of matching and random sample consensus, IEEE transactions on geoscience and remote sensing, Vol. 41, No. 5, pp. 1111-1117.   DOI
3 Kim, M. (2005), The Study on Road Extraction Using LiDAR Data, Master's thesis, Inha University, Incheon, Korea, 62p. (in Korean with English abstract)
4 Kim, S., Yoo, H., and Sohn, K. (2012), FAST and BRIEF based real-time feature matching algorithms, In Proceedings of the Korean Society of Broadcast Engineers Conference, pp. 1-4. (in Korean)
5 Li, Q., Wang, G., Liu, J., and Chen, S. (2009), Robust scaleinvariant feature matching for remote sensing image registration, IEEE Geoscience and Remote Sensing Letters, Vol. 6, No. 2, pp. 287-291.   DOI
6 Lindeberg, T. (2015), Image matching using generalized scalespace interest points, Journal of Mathematical Imaging and Vision, Vol. 52, No. 1, pp. 3-36.   DOI
7 Liu, S., Tong, X., Chen, J., Liu, X., Sun, W., Xie, H., Chen, P., Jin, Y., and Ye, Z. (2016), A linear feature-based approach for the registration of unmanned aerial vehicle remotely-sensed images and airborne LiDAR data, Remote Sensing, Vol. 8, No. 2, 15p.
8 Lowe, D. (2004), Distinctive image features from scaleinvariant keypoints, International Journal of Computer Vision, Vol. 60, No. 2, pp. 91-110.   DOI
9 Matas, J., Chum, O., Urban, M., and Pajdla, T. (2004), Robust wide-baseline stereo from maximally stable extremal regions, Image and Vision Computing, Vol. 22, pp. 761-767.   DOI
10 Mikolajczyk, K. and Schmid, C. (2005), A performance evaluation of local descriptors, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 27, pp. 1615-1630.   DOI
11 Nho, H. (2018), Fast Geocoding Processing for Low-cost Unmanned Aerial Vehicle Imagery, Master's thesis, Yonsei University, Seoul, Korea, 69p.
12 Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., and Van Gool, L. (2005), A Comparison of Affine Region Detectors, International Journal of Computer Vision, Vol. 65, pp. 43-72.   DOI
13 Miksik, O. and Mikolajczyk, K. (2012), Evaluation of local detectors and descriptors for fast feature matching, In Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012), IEEE, pp. 2681-2684.
14 Moravec, H. (1980), Obstacle Avoidance and Navigation in the Real World by a Seeing Robot Rover, No. STAN-CS-80-813, Stanford University, California, USA.
15 Palenichka, R.M. and Zaremba, M.B. (2010), Automatic extraction of control points for the registration of optical satellite and LiDAR images, IEEE Transactions on Geoscience and Remote sensing, Vol. 48, No. 7, pp. 2864-2879.   DOI
16 Park, S., Kim, J., and Yoo, J. (2015), Fast stitching algorithm by using feature tracking, Journal of Broadcast Engineering, Vol. 20, No. 5, pp. 728-737. (in Korean with English abstract)   DOI
17 Abayowa, B.O., Yilmaz, A., and Hardie, R.C. (2015), Automatic registration of optical aerial imagery to a LiDAR point cloud for generation of city models, ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 106, pp. 68-81.   DOI
18 Park, J., Kim, P., Cho, Y.K., and Kang, J. (2019), Framework for automated registration of UAV and UGV point clouds using local features in images, Automation in Construction, Vol. 98, pp. 175-182.   DOI
19 Peng, W.H., Lee, M.Y., Li, T.H., Huang, C.H., and Lin, P.C. (2016), Performance comparison of image keypoint detection, description, and matching methods, In 2016 IEEE 5th Global Conference on Consumer Electronics, IEEE, pp. 1-2.
20 Persad, R.A. and Armenakis, C. (2016), Co-registration of DSMs generated by UAV and terrestrial laser scanning systems, The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. XLI-B1, pp. 985-990.   DOI
21 Tareen, S.A.K. and Saleem, Z. (2018), A comparative analysis of sift, surf, kaze, akaze, orb, and brisk, In 2018 International Conference on Computing, Mathematics and Engineering Technologies (iCoMET), IEEE, pp. 1-10.
22 Rosten, E. and Drummond, T. (2006), Machine learning for high speed corner detection, In 9th Euproean Conference on Computer Vision, Vol. 1, pp. 430-443.
23 Schmind, C., Mohr, R., and Bauckhage, C. (2000), Evaluation of interest point detectors, International Journal of Computer Vision, Vol. 37, No. 2, pp. 151-172.   DOI
24 Shi, J. and Tomasi, C. (1994), Good Features to Track, CVPR.
25 Tsai, C.H. and Lin, Y.C. (2017), An accelerated image matching technique for UAV orthoimage registration, ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 128, pp. 130-145.   DOI
26 Vedaldi, A. and Fulkerson, B. (2010), VLfeat: An open and portable library of computer vision algorithms, In Proceedings of the 18th ACM international conference on Multimedia, Firenze, Italy, pp. 25-29.
27 Alcantarilla, P.F., Bartoli, A., and Davison, A.J. (2012), KAZE features, In European Conference on Computer Vision, Springer, Berlin, Heidelberg, pp. 214-227.
28 Verma, S.B. and Chandran, S. (2016), Comparative Study of FAST MSER and Harris for Palmprint Verification System, International Journal of Scientific & Engineering Research, Vol. 7, No. 12, pp. 855-858.
29 Yang, B. and Chen, C. (2015), Automatic registration of UAVborne sequent images and LiDAR data, ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 101, pp. 262-274.   DOI
30 Abedini, A., Hahn, M., and Samadzadegan, F. (2008), An investigation into the registration of LiDAR intensity data and aerial images using the SIFT approach, In International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. XXXVII, pp. 169-176.
31 Bohm, J. and Becker, S. (2007), Automatic marker-free registration of terrestrial laser scans using reflectance, In Proceedings of the 8th Conference on Optical 3D Measurement Techniques, Zurich, Switzerland, pp. 9-12.
32 Commercializations Promotion Agency for R&D Outcomes (COMPA), (2017), LiDAR Technology and Market Trends, S&T Market Report, Vol. 54, 16p. (in Korean)
33 Conte, G. and Doherty, P. (2008), An integrated UAV navigation system based on aerial image matching, In 2008 IEEE Aerospace Conference, IEEE, pp. 1-10.
34 Fernandez, J.C., Singhania, A., Caceres, J., Slatton, K.C., Starek, M., and Kumar, R. (2007), An Overview of Lidar Point Cloud Processing Software, GEM Center Report No. Rep_2007-12-001, University of Florida, 27p.
35 Fischler, M.A. and Bolles, R.C. (1981), Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography, Communications of the ACM, Vol. 24, No. 6, pp. 381-395.   DOI
36 Harris, C.G. and Stephens, M. (1988), A combined corner and edge detector. In Alvey Vision Conference, Vol. 15, No. 50, pp. 10-5244.
37 Guan, H., Li, J., Yu, Y., Wang, C., Chapman, M., and Yang, B. (2014). Using mobile laser scanning data for automated extraction of road markings, ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 87, pp. 93-107.   DOI