The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part Bl. Beijing 2008 
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4.2 Roll 
Choose to fly through the path vertically back and forth to 
analyze the airline data. Draw a surface that contains the area 
among the generated point cloud data respectively, and build up 
two surfaces. Measure the errors in the vertical orientation of 
the two profiles, and then divide the scanning width of LIDAR 
to come up with a validation value. See the theory in the 
diagram. 
Re-input the value into the LiDAR pre-handling software to re 
generate point cloud data. Use the same way to re-build profiles. 
If those two profiles match well, the validation value for 
Pitching is proved to be valid. If not, go back and redo these 
steps to revise based on the previous validation value until the 
two profiles match completely. According to the steps above, 
not all the profiles data are used in the revising process. 
4.3 Heading 
Choose to two parallel fly airlines in the same direction to 
analyze the airline data and Pitch angle. Draw a block that 
contains the area of the root top among the generated point 
cloud data, and build up two surfaces. Draw a parallel line 
across the roof top as profiles. Measure the errors in the 
horizontal orientation of the two profiles, and then divide the 
extension of two airlines to come up with a heading validation 
value. See the theory in the diagram. 
recording point 
Re-input the value into the LiDAR pre-handling software to re 
generate point cloud data. Use the same way to re-build profiles. 
If those two profiles match well, the validation value for 
Pitching is proved to be valid. If not, go back and redo these 
steps to revise based on the previous validation value until the 
two profiles match completely. According to the steps above, 
not all the profiles data are used in the revising process. 
5. CONCLUSION 
In conclusion, it is not better for more point clouds to view a 
large number of data, but according to the purpose of the 
adjustment from the show adaptation strategies, such man- 
machine interaction can provide speed, but also be very good 
visual effect. 
REFERENCES 
1. D. M. Barber, J. P. Mills and P. G. Bryan. MAINTAINING 
MOMENTUM IN TERRESTRIAL LASER SCANNING: A 
UK CASE STUDY. School of Civil Engineering and 
Geosciences, University of Newcastle upon Tyne, UK. 
2. Dr.-Ing. Rolf Katzenbeisser. ABOUT THE CALIBRATION 
OF LIDAR SENSORS. TopoSys GmbH D-88214 Ravensburg 
3. R. A. Haugerud and D. J. Harding. SOME ALGORITHMS 
FOR VIRTUAL DEFORESTATION (VDF) OF LIDAR 
TOPOGRAPHIC SURVEY DATA. U.S. Geological Survey c/o 
University of Washington. 
4. Jungho Im. Object-based Land Cover Classification Using 
High Posting Density Lidar Data. Environmental Resources and 
Forest Engineering, State University of New York, College of 
Environmental Sciences and Forestry. 
5. E.P. Baltsavias. Airborne laser scanning: basic relations and 
formulas. Institute of Geodesy and Photogrammetry, ETH- 
Hoenggerberg, CH-8093 Zurich, Switzerland. 
6. Xiaohong Zhang. Precise Point Positioning Evaluation and 
Airborne Lidar Calibration. Danish National Space Center.