Determination of Position and Orientation of LiDAR Sensors on Multisensor Platforms
Publication: Journal of Surveying Engineering
Volume 143, Issue 4
Abstract
The use of multisensor system (MSS) plays a key role in engineering geodesy. Because of the complexity of tasks such as industrial applications with high accuracy requirements for heterogeneous and efficient three-dimensional (3D) data acquisition, kinematic MSS are often used. Traditionally, these MSS are composed of referencing sensors and object-capturing sensors. The crucial point in a MSS setup is the determination of the mutual position and orientation [six degrees of freedom (6 DOF)] of each sensor. Within this contribution, a possibility for the determination of the 6 DOF of light detection and ranging (LiDAR) sensors in MSS is introduced. The presented approach is generally applicable and allows the 6 DOF determination of profile laser scanners. The 6 DOF and their uncertainty measures are estimated within an adjustment model by utilizing known reference geometries (RFGs). The approach is especially of interest when sensor origins are not physically available and measurable. It is generally applicable in a static or kinematic measurement environment. As an example, the approach is applied in an industrial environment with accuracy requirements of a few millimeters. The used MSS consists of a terrestrial laser scanner (object capturing) and a laser tracker (referencing). The linking component is a tracker-machine control sensor, which is attached to the laser scanner. More precisely, 6 DOF between the tracker-machine control sensor and the origin of the laser scanner have to be determined. Depending on the required accuracy of the 3D object acquisition, the determination of the 6 DOF must fulfill high accuracy requirements. Finally, the results and the accuracy of the 6 DOF determination are shown and validated with a static calibration procedure.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
The presented methods and results were obtained in the course of the collaborative project “FINISH—Exakte und schnelle Geometrieerfassung sowie Datenauswertung von Schiffsoberflächen für effiziente Beschichtungsprozesse” and are part of the subproject “Entwicklung von Algorithmen und Qualitätsprozessen für ein neuartiges kinematisches terrestrisches Laserscanningsystem” (03SX406D), which is funded by the German Federal Ministry for Economic Affairs and Energy (BMWi). The first, second, and fourth authors acknowledge support with the Leica T-Mac, which was provided by the Institute of Geodesy, Universität der Bundeswehr München. Substantial help was given, in the context of his bachelor thesis, by Lars Marschel.
References
Chow, J., Lichti, D. D., and Glennie, C. (2011). “Point-based versus plane-based self-calibration of static terrestrial laser scanners.” ISPRS Workshop Laser Scanning 2011, D. D. Lichti and A. F. Habib, eds., Vol. XXXVIII-5/W12 of ISPRS Archives, Copernicus, Göttingen, Germany, 121–126.
Dold, C., and Brenner, C. (2006). “Registration of terrestrial laserscanning data using planar patches and image data.” Image Engineering and Vision Metrology, H.-G. Maas and D. Schneider, eds., Vol. XXXVI, Part 5 of ISPRS Archives, Copernicus, Göttingen, Germany, 78–83.
Hexagon Metrology. (2015). “Leica absolute tracker AT960 product brochure.” 〈http://www.leica-geosystems.de/downloads123/m1/metrology/general/brochures/Leica%20AT960%20brochure_en.pdf〉.
Hoag, D. (1963). E-1344 considerations of apollo imu gimbal lock. Apollo guidance and navigation, MIT Instrumentation Laboratory, Cambridge, MA.
JCGM (Joint Committee for Guides in Metrology). (2008). “Evaluation of measurement data: Guide to the expression of uncertainty in measurement.” JCGM 100:2008, GUM 1995 with minor corrections, Bureau International des Poids et Mesures, Sévres, France.
Koch, K.-R. (1999). Parameter estimation and hypothesis testing in linear models, 2nd Ed., Springer, Berlin.
Luhmann, T., Robson, S., Kyle, S., and Boehm, J. (2014). Close-range photogrammetry and 3D imaging, 2nd Ed., De Gruyter, Berlin.
MATLAB [Computer software]. MathWorks, Natick, MA.
Pandey, G., McBride, J. R., Savarese, S., and Eustice, R. M. (2012). “Toward mutual information based automatic registration of 3d point clouds.” Proc., IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, Institute of Electrical and Electronics Engineers (IEEE), New York, 2698–2704.
Pope, A. J. (1972). “Some pitfalls to be avoided in the iterative adjustment of nonlinear problems.” Proc., 38th Annual Meeting, American Society of Photogrammetry, Washington, DC, 449–477.
Rump, S. M. (1999). “Intlab–Interval laboratory.” Developments in reliable computing, T. Csendes, ed., Kluwer Academic, Dordrecht, 77–104.
Shoemake, K. (1985). “Animating rotation with quaternion curves.” SIGGRAPH Comput. Graphics, 19(3), 245–254.
Strübing, T., and Neumann, I. (2013). “Positions- und orientierungsschätzung von lidar-sensoren auf multisensorplattformen.” Zfv, 138(3), 210–221.
Vennegeerts, H. (2011). “Objektraumgestützte kinematische Georeferenzierung für mobile-mapping-systeme.” Ph.D. thesis, Vol. 657 of Reihe C. DGK, Deutsche Geodätische Kommission (DGK), München, Germany.
Vosselman, G., and Maas H.-G., eds. (2010). Airborne and terrestrial laser scanning, Whittles, Dunbeath, Scotland.
Zoller + Froehlich. (2007). “Datasheet Z+F Imager 5006.” Wangen im Allgäu, Germany.
Information & Authors
Information
Published In
Journal of Surveying Engineering
Volume 143 • Issue 4 • November 2017
Copyright
© 2017 American Society of Civil Engineers.
History
Received: Mar 18, 2016
Accepted: Jan 18, 2017
Published online: May 4, 2017
Discussion open until: Oct 4, 2017
Published in print: Nov 1, 2017
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.