Abstract
The calibration and accuracy analysis of a novel, low-cost, adaptable mobile laser scanning (MLS) system using a Velodyne HDL-32E laser scanner and an Oxford Technical Solutions Inertial+2 inertial navigation system, is described. First, a static calibration of the laser scanner is discussed. The static calibration is shown to improve the overall relative accuracy of point cloud data from the scanner by approximately 20% over the manufacturer-supplied calibration. Then, the determination of system boresight angles and lever-arm offsets using a planar patch least-squares approach is presented. Finally, the calibrated and boresighted MLS is operated in a backpack mode to acquire multiple data sets in an area that contains dense ground control acquired using static terrestrial laser scanning (TLS) and a high-end, survey-grade MLS. The dense ground control is used to examine several methods of estimating the overall errors of the backpack MLS system. Detailed comparison of the MLS data with the TLS and survey-grade MLS control shows that, despite the system’s low cost, it is able to reliably collect point cloud data with greater than 10-cm three-dimensional root-mean-square error accuracy.
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Acknowledgments
Support, through a research assistantship from the National Center for Airborne Laser Mapping (NCALM), through a grant from the National Science Foundation’s (NSF) Division of Earth Sciences, Instrumentation and Facilities Program (EAR 1043051), and a teaching assistantship from the Cullen College of Engineering at the University of Houston, for the first author, are acknowledged. The second author was partially supported by a research grant from the Gulf of Mexico Research Initiative (GoMRI). The purchase and development of the mapping-grade mobile laser scanning system was funded by the Public Interest Energy Research (PIER) program of the California Energy Commission (Grant 500-09-035) to the School of Ocean and Earth Sciences and Technology at the University of Hawaii.
References
Barber, D., Mills, J., and Smith-Voysey, S. (2008). “Geometric validation of a ground-based mobile laser scanning system.” ISPRS J. Photogramm. Remote Sens., 63(1), 128–141.
Brooks, B., Glennie, C., Hudnut, K., Ericksen, T., and Hauser, D. (2013). “Mobile laser scanning applied to the Earth sciences.” Eos, Trans. AGU, 94(36), 313–315.
CloudCompare [Computer software]. Daniel Girardeau-Montaut.
DELPH INS [Computer software]. iXBlue, Saint-Germain en Laye, France.
Glennie, C. (2007). “Rigorous 3D error analysis of kinematic scanning LIDAR systems.” J. Appl. Geod., 1(3), 147–157.
Glennie, C. (2009). “Kinematic terrestrial light-detection and ranging system for scanning.” Transportation Research Record, 2105, 135–141.
Glennie, C. (2012). “Calibration and kinematic analysis of the Velodyne HDL-64E S2 lidar sensor.” Photogramm. Eng. Remote Sens., 78(4), 339–347.
Glennie, C., et al. (2013). “Compact multipurpose mobile laser scanning system–Initial tests and results.” Remote Sens., 5(2), 521–538.
Glennie, C., and Lichti, D. (2010). “Static calibration and analysis of the Velodyne HDL-64E S2 for high accuracy mobile scanning.” Remote Sens., 2(6), 1610–1624.
Glennie, C., and Lichti, D. (2011). “Temporal stability of the Velodyne HDL-64E S2 scanner for high accuracy scanning applications.” Remote Sens., 3(3), 539–553.
Haala, N., Peter, M., Kremer, J., and Hunter, G. (2008). “Mobile LIDAR mapping for 3D point cloud collection in urban areas—A performance test.” Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., 37, 1119–1124.
Hunter, G., Cox, C., and Kremer, J. (2006). “Development of a commercial laser scanning mobile mapping system–StreetMapper.” Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., 36.
Jaakkola, A., et al. (2010). “A low-cost multi-sensoral mobile mapping system and its feasibility for tree measurements.” ISPRS J. Photogramm. Remote Sens., 65(6), 514–522.
Kaartinen, H., Hyyppä, J., Kukko, A., Jaakkola, A., and Hyyppä, H. (2012). “Benchmarking the performance of mobile laser scanning systems using a permanent test field.” Sensors, 12(9), 12814–12835.
Kukko, A., Kaartinen, H., Hyyppä, J., and Chen, Y. (2012). “Multiplatform mobile laser scanning: Usability and performance.” Sensors, 12(9), 11712–11733.
Leica Cyclone [Computer software]. Leica Geosystems, Heerbrugg, Switzerland.
Lin, Y., Hyyppä, J., and Jaakkola, A. (2011). “Combining mobile and static terrestrial laser scanners to investigate individual crown attributes during foliation.” Can. J. Remote Sens., 37(4). 359–375.
Lin, Y., Jaakkola, A., Hyyppä, J., and Kaartinen, H. (2010). “From TLS to VLS: Biomass estimation at individual tree level.” Remote Sens., 2(8), 1864–1879.
Olsen, M., et al. (2013). Guidelines for the use of mobile LIDAR in transportation applications, National Academy of Sciences, Washington, DC.
OPUS-RS [Computer software]. National Geodetic Survey, Silver Spring, MD.
OxTS. (2009). “Inertial+ datasheet.” 〈http://www.oxts.com〉 (Mar. 12, 2014).
Puente, I., González-Jorge, H., Riveiro, B., and Arias, P. (2013). “Accuracy verification of the Lynx Mobile Mapper system.” Opt. Laser Technol., 45, 578–586.
RiSCAN PRO [Computer software]. RIEGL LMS, Horn, Austria.
RT Post-Process [Computer software]. Oxford Technical Solutions, Oxfordshire, U.K.
Schaer, P., Skaloud, J., Landtwing, S., and Legat, K. (2007) “Accuracy estimation for laser point cloud including scanning geometry.” Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., 36.
Shakarji, C. (1998). “Least squares fitting algorithms of the NIST algorithm testing system.” J. Res. Natl. Inst. Stand. Technol., 103(6), 633–641.
Skaloud, J., and Lichti, D. (2006). “Rigorous approach to bore-sight self-calibration in airborne laser scanning.” ISPRS J. Photogramm. Remote Sens., 61(1), 47–59.
Topcon Tools [Computer software]. Topcon Positioning Sytems, Livermore, CA.
Vaaja, M., Hyyppä, J., Kukko, A., Kaartinen, H., Hyyppä, H., and Alho, P. (2011). “Mapping topography changes and elevation accuracies using a mobile laser scanner.” Remote Sens., 3(3), 587–600.
Vaaja, M., et al. (2013). “Data processing and quality evaluation of a boat-based mobile laser scanning system.” Sensors, 13(9), 12497–12515.
Velodyne. (2013). HDL-32E datasheet. 〈http://www.velodynelidar.com〉 (May 3, 2014).
Wallace, L., Lucieer, A., Watson, C., and Turner, D. (2012). “Development of a UAV-LiDAR system with application to forest inventory.” Remote Sens., 4(12), 1519–1543.
Waypoint GrafNav [Computer software]. NovAtel, Calgary, AB, Canada.
Yen, K., Akin, K., Lofton, A., Ravani, B., and Lasky, T. (2010). Using mobile laser scanning to produce digital terrain models of pavement surfaces, Advanced Highway Maintenance and Construction Technology Research Center, Davis, CA.
Young, A., et al. (2010). “Comparison of airborne and terrestrial lidar estimates of seacliff erosion in southern California.” Photogramm. Eng. Remote Sens., 76(4), 421–427.
Information & Authors
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Published In
Journal of Surveying Engineering
Volume 142 • Issue 4 • November 2016
Copyright
© 2016 American Society of Civil Engineers.
History
Received: Oct 9, 2015
Accepted: Dec 10, 2015
Published online: Feb 10, 2016
Discussion open until: Jul 10, 2016
Published in print: Nov 1, 2016
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