1. AUTOMATIC TARGET IDENTIFICATION FOR LASER SCANNERS Artemis Valanis, Maria Tsakiri National Technical University Of Athens

5. Repeatability Results Average standard deviation of mean position (mm) EDM Baseline targets Wall targets Mean pos. Radiometric pos. Mean pos. Radiometric pos. X 0.154 0.190 0.025 0.034 Y 0.113 0.217 0.118 0.073 Z 0.228 0.267 0.058 0.090

8. Automatic target identification

11. New algorithms Fuzzy classification into three reflectivity classes Centre: average of X,Y,Z of the points of the two classes of highest average reflectance Fuzzypos Plane fitting, system transformation and data selection Centre: average X,Y and Z of the points of the lowest reflectance class points transformed back to the original system Gridrad & Delrad Creation of surface and reflectance models (5mm spacing) Centre: The radiometric centre calculated using the data of the two grids Fuzzygridrad & Fuzzydelrad Same as gridrad and delrad but instead of the radcent the fuzzypos algorithm is used Fuzzypos Fuzzyposfine

12. Algorithm Internal Accuracy Evaluation Two experiments EDM baseline targets Wall targets Multiple scan collection from two positions Multiple scan collection from one position

13. Algorithm Internal Accuracy Evaluation For each position Reference data: Single scan Test data: Single and multiple scans collected from the same position Mean Absolute Error calculation for each data series Mean Error for each experiment algorithms Transformation between the results of the reference and test data

14. Results for the estimation of the internal accuracy of the algorithms examined

16. Results for the evaluation of the external accuracy of the algorithms (EDM baseline targets)

17. Results for the evaluation of the external accuracy of the algorithms (Wall targets)

18. 3m distance

20. Thank you for your attention!