Automatic Target Identification
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The document summarizes research on improving automatic target identification methods for laser scanners. It presents results from testing new fuzzy classification algorithms on targets scanned with a Cyrax 2500 laser scanner. The new fuzzypos and fuzzyposfine algorithms achieved the best performance, with high identification accuracy under 1mm, for various scan angles and distances. These algorithms utilize fuzzy classification of target point clouds and provide improved and more robust automatic target identification compared to existing methods.
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And
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1Faculty of Eng. Suez Canal University, Ismailia, Egypt
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AIAA-MAO-RegionalError-2012
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Project portfolio Artemis Valanis
Work carried out by Artemis Valanis at the Laboratory of Photogrammetry of NTUA and as a freelance professional
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This document proposes a markerless registration method for registering partial scans of free-form objects. It describes a constrained acquisition process where scans are taken with a small amount of overlap by rotating the scan head vertically or horizontally. The method samples the unknown rotation angle space to approximate the relative transformation between scans. It evaluates the median distance between overlapping points to find the best alignment. The algorithm is validated on scan data and shown to initialize ICP registration accurately without markers or features.
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Automatic Target Identification
- 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
- 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!