Startseite Target-based terrestrial laser scan registration extended by target orientation
Artikel
Lizenziert
Nicht lizenziert Erfordert eine Authentifizierung

Target-based terrestrial laser scan registration extended by target orientation

  • Jannik Janßen EMAIL logo , Heiner Kuhlmann und Christoph Holst
Veröffentlicht/Copyright: 2. Dezember 2021
Veröffentlichen auch Sie bei De Gruyter Brill

Abstract

In almost all projects, in which terrestrial laser scanning is used, the scans must be registered after the data acquisition. Despite more and more new and automated methods for registration, the classical target-based registration is still one of the standard procedures. The advantages are obvious: independence from the scan object, the geometric configuration can often be influenced and registration results are easy to interpret. When plane black-and-white targets are used, the algorithm for estimating the target center fits a plane through the scan of a target, anyway. This information about the plane orientation has remained unused so far. Hence, including this information in the registration does not require any additional effort in the scanning process.

In this paper, we extend the target-based registration by the plane orientation. We describe the required methodology, analyze the benefits in terms of precision and reliability and discuss in which cases the extension is useful and brings a relevant advantage. Based on simulations and two case studies we find out that especially for registrations with bad geometric configurations the extension brings a big advantage. The extension enables registrations that are much more precise. These are also visible on the registered point clouds. Thus, only a methodological change in the target-based registration improves its results.

Acknowledgment

The authors would like to thank Tomislav Medić for his support in the data acquisition for the case study Basement.

References

[1] D. Akca. Full automatic registration of laser scanner point clouds. Technical report, ETH Zurich, 2003.Suche in Google Scholar

[2] D. Akca. Matching of 3d surfaces and their intensities. ISPRS Journal of Photogrammetry and Remote Sensing, 62(2):112–121, 2007.10.1016/j.isprsjprs.2006.06.001Suche in Google Scholar

[3] S. Barnea and S. Filin. Keypoint based autonomous registration of terrestrial laser point-clouds. ISPRS Journal of Photogrammetry and Remote Sensing, 63(1):19–35, 2008.10.1016/j.isprsjprs.2007.05.005Suche in Google Scholar

[4] P. Besl and N. McKay. A method for registration of 3-d shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14(2):239–256, 1992.10.1109/34.121791Suche in Google Scholar

[5] J. Chow, A. Ebeling, and B. Teskey. Low cost artificial planar target measurement techniques for terrestrial laser scanning. In Proceedings of the FIG Congress 2010: Facing the Challenges–Building the Capacity. Citeseer, 2010.Suche in Google Scholar

[6] C. Dold and C. Brenner. Registration of terrestrial laser scanning data using planar patches and image data. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences – ISPRS Archives 36 (2006), 36:78–83, 2006.Suche in Google Scholar

[7] M. Eslami and M. Saadatseresht. A new tie plane-based method for fine registration of imagery and point cloud dataset. Canadian Journal of Remote Sensing, 46(3):295–312, 2020.10.1080/07038992.2020.1785282Suche in Google Scholar

[8] W. Förstner. Reliability analysis of parameter estimation in linear models with applications to mensuration problems in computer vision. Computer Vision, Graphics, and Image Processing, 40(3):273–310, 1987.10.1016/S0734-189X(87)80144-5Suche in Google Scholar

[9] W. Förstner and B. P. Wrobel. Photogrammetric computer vision. Springer, 2016.10.1007/978-3-319-11550-4Suche in Google Scholar

[10] W. Förstner and K. Khoshelham. Efficient and accurate registration of point clouds with plane to plane correspondences. In Proceedings of the IEEE International Conference on Computer Vision Workshops, pages 2165–2173, 2017.10.1109/ICCVW.2017.253Suche in Google Scholar

[11] A. Gruen and D. Akca. Least squares 3d surface and curve matching. ISPRS Journal of Photogrammetry and Remote Sensing, 59(3):151–174, 2005.10.1016/j.isprsjprs.2005.02.006Suche in Google Scholar

[12] F. R. Helmert. Die mathematischen und physikalischen Theorien der Höheren Geodäsie, Band I. Verlag Teubner, Leipzig, 1880.Suche in Google Scholar

[13] C. Holst, L. Klingbeil, F. Esser, and H. Kuhlmann. Using point cloud comparisons for revealing deformations of natural and artificial objects. In Proceedings of the 7th International Conference on Engineering Surveying (INGEO 2017), Lisbon, Portugal, pages 18–20, 2017.Suche in Google Scholar

[14] J. Janßen, T. Medic, H. Kuhlmann, and C. Holst. Decreasing the uncertainty of the target center estimation at terrestrial laser scanning by choosing the best algorithm and by improving the target design. Remote Sensing, 11(7):845, 2019.10.3390/rs11070845Suche in Google Scholar

[15] T. Jurek, H. Kuhlmann, and C. Holst. Impact of spatial correlations on the surface estimation based on terrestrial laser scanning. Journal of Applied Geodesy, 11(3):143–155, 2017.10.1515/jag-2017-0006Suche in Google Scholar

[16] S. Kauker, C. Holst, V. Schwieger, H. Kuhlmann, and S. Schön. Spatio-temporal correlations of terrestrial laser scanning. Allgemeine Vermessungs Nachrichten (AVN), 6:170–182, 2016.Suche in Google Scholar

[17] M. Kavouras. On the detection of outliers and the determination of reliability in geodetic networks, Report no. 87. Department of Geodesy and Geomatics Engineering, University of New Brunswick, 1985.Suche in Google Scholar

[18] K. Kregar, D. Grigillo, and D. Kogoj. High precision target center determination from a point cloud. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, II(5/W2):139–144, 2013.10.5194/isprsannals-II-5-W2-139-2013Suche in Google Scholar

[19] D. Lague, N. Brodu, and J. Leroux. Accurate 3D comparison of complex topography with terrestrial laser scanner: Application to the Rangitikei Canyon (NZ). ISPRS Journal of Photogrammetry and Remote Sensing, 82:10–26, 2013.10.1016/j.isprsjprs.2013.04.009Suche in Google Scholar

[20] Leica Geosystems. Data sheet for Leica ScanStation P50. www.leica-geosystems.com, 2017. Last call: 8th July 2020.Suche in Google Scholar

[21] T. Medić, C. Holst, J. Janßen, and H. Kuhlmann. Empirical stochastic model of detected target centroids: Influence on registration and calibration of terrestrial laser scanners. Journal of Applied Geodesy, 13(3):179–197, 2019.10.1515/jag-2018-0032Suche in Google Scholar

[22] T. Medić, H. Kuhlmann, and C. Holst. Designing and evaluating a user-oriented calibration field for the target-based self-calibration of panoramic terrestrial laser scanners. Remote Sensing, 12(1):15, 2020.10.3390/rs12010015Suche in Google Scholar

[23] F. Pomerleau, F. Colas, R. Siegwart, and S. Magnenat. Comparing icp variants on real-world data sets. Autonomous Robots, 34(3):133–148, 2013.10.1007/s10514-013-9327-2Suche in Google Scholar

[24] M. Previtali, L. Barazzetti, R. Brumana, and M. Scaioni. Scan registration using planar features. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 40(5):501, 2014.10.5194/isprsarchives-XL-5-501-2014Suche in Google Scholar

[25] A. Rietdorf. Automatisierte Auswertung und Kalibrierung von scannenden Messsystemen mit tachymetrischem Messprinzip. PhD thesis, Technische Universität Berlin, DGK (German Geodetic Commission) C 582, 2005.Suche in Google Scholar

[26] S. Rusinkiewicz and M. Levoy. Efficient variants of the icp algorithm. In Proceedings Third International Conference on 3-D Digital Imaging and Modeling, pages 145–152. IEEE, 2001.10.1109/IM.2001.924423Suche in Google Scholar

[27] R. B. Rusu, N. Blodow, and M. Beetz. Fast point feature histograms (fpfh) for 3d registration. In 2009 IEEE International Conference on Robotics and Automation, pages 3212–3217. IEEE, 2009.10.1109/ROBOT.2009.5152473Suche in Google Scholar

[28] B. Schmitz, H. Kuhlmann, and C. Holst. Investigating the resolution capability of terrestrial laser scanners and its impact on the effective number of measurements. ISPRS Journal of Photogrammetry and Remote Sensing, 159:41–52, 2020.10.1016/j.isprsjprs.2019.11.002Suche in Google Scholar

[29] M. Staudinger. A Cost Orientated Approach to Geodetic Network Optimisation. Vienna, University of Technology. PhD thesis, Faculty of Technical Science, University of Technology, Vienna, 1999.Suche in Google Scholar

[30] P. W. Theiler, J. D. Wegner, and K. Schindler. Keypoint-based 4-points congruent sets–automated marker-less registration of laser scans. ISPRS Journal of Photogrammetry and Remote Sensing, 96:149–163, 2014.10.1016/j.isprsjprs.2014.06.015Suche in Google Scholar

[31] D. Wujanz, S. Schaller, F. Gielsdorf, and L. Gründig. Plane-based registration of several thousand laser scans on standard hardware. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, 42(2), 2018.10.5194/isprs-archives-XLII-2-1207-2018Suche in Google Scholar

[32] R. Yang, X. Meng, Z. Xiang, Y. Li, Y. You, and H. Zeng. Establishment of a new quantitative evaluation model of the targets’ geometry distribution for terrestrial laser scanning. Sensors, 20(2):555, 2020.10.3390/s20020555Suche in Google Scholar PubMed PubMed Central

[33] Q.-Y. Zhou, J. Park, and V. Koltun. Fast global registration. In European Conference on Computer Vision, pages 766–782. Springer, 2016.10.1007/978-3-319-46475-6_47Suche in Google Scholar

Received: 2020-07-08
Accepted: 2021-10-27
Published Online: 2021-12-02
Published in Print: 2022-04-26

© 2022 Walter de Gruyter GmbH, Berlin/Boston

Heruntergeladen am 22.9.2025 von https://www.degruyterbrill.com/document/doi/10.1515/jag-2020-0030/html
Button zum nach oben scrollen