||Multi-station Network Adjustment of Terrestrial LiDAR Data with GPS Positioning
||Department of Geomatics
Point cloud registration
Terrestrial LiDAR is the state of the art technology of collecting 3D spatial data. Point clouds delivered by a terrestrial LiDAR are referenced to a local coordinate system defined by the laser scanner. For most applications, raw point clouds should be transformed into a global coordinate system, i.e., geo-referencing of point clouds, to match the need for practical applications. The geo-referencing of terrestrial LiDAR data is currently relied on known control points. However the acquisition of control points for geo-referencing is often troublesome and laborious since the field surveying is usually required for obtaining very high accuracy control points. A direct geo-referencing system, which is necessarily employed in a mobile LiDAR system, is certainly too expensive to the use of a terrestrial LiDAR. However, it is quite common for a modern terrestrial LIDAR to have a mount for GPS antenna. It means that the precise position of a scan station can be obtained in the field. Under this circumstance, geo-referencing of terrestrial LiDAR data can be achieved by combining several (at least three) overlapping point clouds scanned from multiple stations. This is the idea motivates this study.
This paper presents a methodology to achieve geo-referencing with few or even no control points. In our strategy, we utilize the observation equations based on the geometric relationship between the center of GPS antenna, terrestrial LiDAR, and point features to perform multi-station adjustment. For the experiments, there are two test field involved, one is a well-controlled test field in the Tzu-Chiang Campus of National Cheng Kung University, and the other case is Eternal Golden Castle in Tainan city. The test data of these two cases were processed with the manufacture software, and the proposed network adjustment model. In the Tzu-Chiang Campus case, we tested three conditions of network adjustment, which are adjustment with control points only, with GPS positions only, and with both of them. The results show that the accuracy of check points in network adjustment with GPS observations only can achieve about 2 centimeters in E and N directions, and about 7 centimeters in H direction. In the Eternal Golden Castle case, where the landscape is much more complicated. The accuracy of check points in network adjustment with GPS observations only can achieve about 4 centimeters in E and N directions, and about 7 centimeters in H direction. The results show that the accuracy of network adjustment with GPS observations only are quite well enough for applications.
List of Tables VII
List of Figures IX
Chapter 1. Introduction 1
1.1 Motivation 1
1.2 Objective 4
1.3 Research Approach 5
1.4 Thesis Structure 6
Chapter 2. Integration and Registration of Terrestrial LiDAR Data 8
2.1 Coordinate System of Terrestrial LiDAR 8
2.2 Methods of point cloud data integration and registration 10
2.2.1 Point cloud-based 10
2.2.2 Feature-based 12
Chapter 3. Multi-station Network adjustment with GPS observation 15
3.1 Geometric Relationships 15
3.2 Multi-station Network adjustment with control points 20
3.3 The use of GPS observations 26
Chapter 4. Experiments 30
4.1 Test field in NCKU Campus 30
4.1.1 Data collection 31
4.1.2 Set up of check points 33
4.1.3 Data processing using commercial software (RiSCAN) 37
188.8.131.52 The work flow 37
184.108.40.206 Limitation of the software (RiSCAN) 38
220.127.116.11 Results of derived by RiSCAN 39
4.1.4 Results of network adjustment 41
4.1.5 Influence of scanning station distribution 48
4.2 Case study of Eternal Golden Castle 49
4.2.1 Data collection 49
4.2.2 Set up of check points 51
4.2.3 Data processing with commercial software 52
4.2.4 Results of network adjustment 54
4.3 Analysis and Discussion 59
Chapter 5. Conclusions 60
Akca, D., “Fully Automatic Registration of Laser Scanner Point Clouds”, In: Optical 3-D Measurement Techniques VI, volume 1, Zurich, Switzerland, pp.330-337, 2003.
Alamús, R., Baron, A., Bosch, E., Casacuerta, J., Miranda, J., Pla, M., Sànchez, S., Serra,A. and Talaya, J. “ON THE ACCURACY AND PERFORMANCE OF THE GEOMÒBIL SYSTEM”, International Archives of Photogrammetry and Remote Sensing, Istambul, Turkey, 2004
Baeder, B., Osborn, C. and Rhea, J., “Low Cost Navigation Technology Investigation for the Unmanned Ground Vehicle Program”, In Proceedings of the Position Location and Navigation Symposium, Las Vegas, NV, USA, pp. 574-580., April 1994.
Besl, P.J. and McKay, N.D., “A Method for Registration of 3-D Shape”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 14(2):239-256, 1992.
Chuang, T.Y., “Feature-Based Registration of LiDAR Point Clouds”(in Chinese), Doctoral Dissertation, Department of Civil Engineering College of Engineering, National Taiwan University, Taiwan, 2012.
Chuang, T.Y., “Registration of Terrestrial LidAR Point Clouds by Means of 3D Line Features”, Journal of Photogrammetry and Remote Sensing Volume 13, No.3, Sepetmber 2008, pp. 169-182, 2008.
Godin, G., Laurendeau, D. and Bergevin, R., “A Method for the Registration of Attributed Range Images”, Third International Conference on 3D Imaging and Modeling, Quebec, Canada, May 28-June 1,2001,pp. 179-186, 2001.
Gressin, A., Mallet, C., Demantké, J. and David, N., “Towards 3D lidar point cloud registration improvement using optimal neighborhood knowledge”, ISPRS Journal of Photogrammetry and Remote Sensing 79 (2013) 240–251, 2013.
Gruen, A. and Akca, D., “Least Squares 3-D Surface Matching”, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 34, part5/W16, 2005
Habib, A., Ghanma,M. Morgan, M. and Al-Ruzouq, R., “Photogrammetric and LiDAR Data Registration Using Linear Features”, American Society for Photogrammetry and Remote Sensing, Vol. 71, No. 6, pp699-707, 2005.
Huang, C.M., “Multi-station Registration and Adjustment of Terrestrial LiDAR Data Using Point, Line and Plane Features” (in Chinese), Master Thesis, Department of Geomatics, National Cheng Kung University, Taiwan, 2009.
Konno,T., Kono, K., Fujimoto,T. and Chiba,N., “Point Cloud Registation Based on Features Lines Derived from Depth Difference”, Proc. IWAIT2005, pp499-504, 2005.
Kuo, L.C.,“The Application of Ground Based LiDAR for landslide Topographic Mapping” (in Chinese), Master Thesis, Department of Civil Engineering, National Chiao Tung University, Hsinchu, Taiwan, June 2004.
Lee, S.Y, “Extracting Corner Feature Points for Registration of Multi-Station Point Cloud Data” (in Chinese), Master Thesis, Department of Geomatics, National Cheng Kung University, Taiwan, 2005.
Lin, W.H., “Application of 3D Laser Scanning on Land Parcel Survey” (in Chinese), Master Thesis, Department of Earth Sciences, National Chung Kung University, Taiwan,2007.
Liu, T.L., “Point Cloud Adjustment Merging and Image Mapping for Ground-Based Lidar” (in Chinese), Master Thesis, Department of Geomatics, National Cheng Kung University, Taiwan, 2003.
Mohamed A., Wilkinson B., “Direct Georeferencing of Stationary LiDAR”, Remote Sens. 2009, 1, 1321-1337; doi:10.3390/rs1041321, 2009.
National Land Surveying and Mapping Center, “National Service manual of constructing 1/1000 digital topographic map by photogrammetry in urban area”, 2010 (內政部，2010。建置都會區一千分之ㄧ數值航測地形圖作業工作手冊。 )
Reshetyuk, Y., “Direct Georeferencing with GPS in Terrestrial Laser Scanning”, ZFV - Zeitschrift fur Geodasie, Geoinformation und Landmanagement 01/2010; 135(3):151-159., 2010.
Riegl, “3D Terrestrial Laser Scanner Riegl VZ-400/ Tiegl VZ-100 General Description and Data Interfaces”, pp 481, pp 472-475, 2012
Schuhmacher, S. and Böhm, J., “Georeferencing of Terrestrial Laserscanner Data for Applications in Architectural Modelling”, 3D-ARCH 2005: "Virtual Reconstruction and Visualization of Complex Architectures" Mestre-Venice, Italy, 2005.
Sharp,G.C., Lee, S.W. and Ehe, D.K., “ICP Registration Using Invariant Features”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(1):90-102, 2002.
Tang, K.P., “Adjustment Model of LIDAR Data Merging Based on Plane Features” (in Chinese), Master Thesis, Department of Geomatics, National Cheng Kung University, Taiwan, 2009.
Wang,T., “Iterative Closest Point algorithm-point cloud/mesh registration”, http://taylorwang.wordpress.com/2012/04/06/iterative-closest-point-algorithm-point-cloudmesh-registration/ (accessed on 17 June 2014)
Wu, M.S., Chen, K.W. and Huang, C.H., “Application of Topography Survey by Terrestrial 3D Laser Scanner for Torrent and Slope – A case of Laishe Creek and Poly Creek, PingTung, Taiwan” (in Chinese), 2013 Soil and Water Conservation Society Annual Conference, 2013.