Automatic Target Identification


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Automatic Target Identification

  1. 1. AUTOMATIC TARGET IDENTIFICATION FOR LASER SCANNERS Artemis Valanis, Maria Tsakiri National Technical University Of Athens
  2. 2. Problem identification <ul><li>Demand for the highest possible accuracy in all kinds of applications (especially when registration is necessary) </li></ul><ul><li>Is there a way to estimate the repeatability of the measurements? </li></ul><ul><li>Could automatic target identification be improved? </li></ul><ul><li>Do currently used methods have drawbacks? </li></ul><ul><li>Poor documentation of proprietary software </li></ul>
  3. 3. Objectives <ul><li>Repeatability check for measurements obtained with a Cyrax 2500 laser scanner </li></ul><ul><li>Development of new methods for automatic target identification </li></ul><ul><li>Comparison of old and new methods </li></ul>
  4. 4. Repeatability check <ul><li>Data: </li></ul><ul><ul><li>9 scans of four targets mounted on the pillars of the EDM calibration baseline of the NTUA </li></ul></ul><ul><ul><li>9 scans of five targets mounted on a wall </li></ul></ul><ul><li>Processing: </li></ul><ul><ul><li>Mean and weighted (radiometric) position using each one of the scans for each target </li></ul></ul><ul><ul><li>Standard deviation of the mean and weighted mean values for each one of the targets </li></ul></ul><ul><ul><li>Average standard deviation for all of the targets in both cases </li></ul></ul>
  5. 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
  6. 6. Automatic target identification <ul><li>Currently used methods </li></ul><ul><ul><li>maxrad: position of maximum signal strength </li></ul></ul><ul><ul><li>maxrad4: radiometric centre of the four points of maximum signal strength </li></ul></ul><ul><ul><li>radcent: radiometric centre of all returns </li></ul></ul>
  7. 7. Automatic target identification <ul><li>radcent </li></ul><ul><li>maxrad </li></ul><ul><li>maxrad4 </li></ul><ul><li>reflective area of the target </li></ul>
  8. 8. Automatic target identification
  9. 9. Target examination <ul><li>Use of fuzzy classification for examination of the properties of the targets </li></ul><ul><li>Utilization of the fuzzy c-means method to classify the points of a point-cloud of a target into 3 classes based on their reflectivity </li></ul>
  10. 10. Target examination Scan angle: 90 o Scan angle: 45 o Scan angle: 15 o <ul><li>low reflectance </li></ul><ul><li>medium reflectance </li></ul><ul><li>high reflectance </li></ul>
  11. 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. 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. 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. 14. Results for the estimation of the internal accuracy of the algorithms examined
  15. 15. Algorithm External Accuracy Evaluation Mean error EDM baseline targets Wall targets <ul><li>Reference /Test data: </li></ul><ul><li>Fine scan </li></ul><ul><li>Single scan </li></ul><ul><li>Four merged scans </li></ul><ul><li>(two positions) </li></ul>Reference data: 4 merged scans (90 o ) Test data: 6 data series 3 & 10 m (dist) 90 o , 45 o & 15 o Mean absolute error
  16. 16. Results for the evaluation of the external accuracy of the algorithms (EDM baseline targets)
  17. 17. Results for the evaluation of the external accuracy of the algorithms (Wall targets)
  18. 18. 3m distance
  19. 19. Conclusions <ul><li>The repeatability of the measurements obtained with a Cyrax 2500 laser scanner is very high </li></ul><ul><li>Fuzzy classification is a valuable tool for obtaining a meaningful model of the data collected </li></ul><ul><li>The fuzzypos and fuzzyposfine algorithms: </li></ul><ul><ul><li>Best performance for all combinations of scan angles and distances </li></ul></ul><ul><ul><li>Results of high accuracy (<1mm) </li></ul></ul><ul><ul><li>Off-line processing possible </li></ul></ul><ul><ul><li>Algorithms available </li></ul></ul>
  20. 20. Thank you for your attention!