Abstract
The technique of Image Assisted Total Stations (IATS) has been studied for over ten years and is composed of two major parts: one is the calibration procedure which combines the relationship between the camera system and the theodolite system; the other is the automatic target detection on the image by various methods of photogrammetry or computer vision. Several calibration methods have been developed, mostly using prototypes with an add-on camera rigidly mounted on the total station. However, these prototypes are not commercially available. This paper proposes a calibration method based on Leica MS50 which has two built-in cameras each with a resolution of 2560 × 1920 px: an overview camera and a telescope (on-axis) camera. Our work in this paper is based on the on-axis camera which uses the 30-times magnification of the telescope. The calibration consists of 7 parameters to estimate. We use coded targets, which are common tools in photogrammetry for orientation, to detect different targets in IATS images instead of prisms and traditional ATR functions. We test and verify the efficiency and stability of this monitoring method with multi-target.
Funding source: China Scholarship Council
Award Identifier / Grant number: 201406260148
References
[1] Wagner, A. (2016): A new approach for geo-monitoring using modern total stations and RGB+D images. Measurement, Vol. 82, pp. 64–74.10.1016/j.measurement.2015.12.025Search in Google Scholar
[2] Leica Geosystems AG. (2013). Leica MS50/TS50/TM50 – User Manual. User manual, v1.1.1, 84 pages.Search in Google Scholar
[3] Walser, B. (2004): Development and calibration of an image assisted total station. PhD Thesis, ETH-Zürich.Search in Google Scholar
[4] Knoblach, S. (2009): Entwicklung, Kalibrierung und Erprobung eines kameraunterstützten Hängetachymeters. PhD Thesis, Technische Universität Dresden.Search in Google Scholar
[5] Bürki, B.; Guillaume, S.; Sorber, P.; Oesch, H. S. (2010): DAEDALUS: A versatile usable digital clip-on measuring system for total stations. In: Indoor Positioning and Indoor Navigation (IPIN), 2010 International Conference on, Zurich, September 15–17, pp. 1–10.10.1109/IPIN.2010.5646270Search in Google Scholar
[6] Hattori, S.; Akimoto, K.; Fraser, C.; Imoto, H. (2002): Automated procedures with coded targets in industrial vision metrology. Photogrammetric Engineering & Remote Sensing, 68(5), pp. 441–446.Search in Google Scholar
[7] Hattori, S.; Akimoto, K.; Fraser, C.; Ono, T.; Imoto, H. (2000): Design of coded targets and automated measurement procedures in industrial vision metrology. International Archives of Photogrammetry and Remote Sensing, Vol. XXXIII, Supplement B5.Search in Google Scholar
[8] Bradski, G. (2000): The OpenCV Library. Dr. Dobb’s Journal of Software Tools.Search in Google Scholar
[9] Huang, Y. D.; Harley, I. (1989); A new camera calibration method needing no control field. Optical 3-D measurement techniques. Editors Gruen and Kahmen. Wichmann, Karlsruhe, pp. 49–56.Search in Google Scholar
[10] Devernay, F.; Faugeras, O. (2001): Straight lines have to be straight: automatic calibration and removal of distortion from scenes of structured environment. Machine Vision and Applications, 13(1), pp. 14–24.10.1007/PL00013269Search in Google Scholar
[11] Tsai, R. Y. (1987): A versatile camera calibration technique for high accuracy 3D machine vision. International Journal of Robotics and Automation, 3(4), pp. 323–344.10.1109/JRA.1987.1087109Search in Google Scholar
[12] Wasmeier, P. (2009): Grundlagen der Deformations- bestimmung mit Messdaten bildgebender Tachymeter. PhD Thesis, Technische Universität München.Search in Google Scholar
[13] Reiterer, A.; Wagner, A. (2012): System consideration of an image assisted total station – evaluation and assessment. In: Allgemeine Vermessungsnachrichten (avn), 119(3), pp. 83–94.Search in Google Scholar
[14] Ahn, S. J.; Rauh, W. (1998): Circular coded target and its application to optical 3D-measurement techniques. In: Proc. 20. DAGM Symp. Mustererkennung, pp. 245–252.10.1007/978-3-642-72282-0_26Search in Google Scholar
[15] Canny, J. (1986): A computational approach to edge detection. In: IEEE Trans. Pattern Analysis and Machine Intelligence, 8(6), pp. 679–698.10.1016/B978-0-08-051581-6.50024-6Search in Google Scholar
[16] Zhou, Y.; Wagner, A.; Wunderlich, Th.; Wasmeier, P. (2015): Close Range Angles Deformation Monitoring by Telescope Camera of Total Station MS50 using Automatic Detection of Coded Targets. Proceedings of 2nd Int. Workshop on Civil Engineering and Architecture, pp. 153–162.Search in Google Scholar
[17] Luhmann, T. (2014): Eccentricity in images of circular and spherical targets and its impact on spatial intersection. The Photogrammetric Record, 29(148), December 2014, pp. 417–433.10.1111/phor.12084Search in Google Scholar
[18] Wagner, A.; Wasmeier, P. (2014): Flächen-und Feature- basiertes Monitoring mit Videotachymetern. Multi- Sensor-Systeme – Bewegte Zukunftsfelder. Schriftreihe des DVW, Vol. 75, pp. 75–88.Search in Google Scholar
© 2017 Walter de Gruyter GmbH, Berlin/Boston
