Object Indentification Using 3D SketchUp Models in Environment Scans


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Abstract. This work introduces a novel solution for localizing objects based on search strings and freely available Google SketchUp models. To this end we automatically download and preprocess a collection of 3D models to obtain equivalent point clouds. The outdoor scan is segmented into individual objects, which are sequentially matched with the models by a variant of iterative closest points algorithm using seven degrees of freedom and resulting in a highly precise pose estimation of the object. An error function evaluates the similarity level. The approach is verified using various segmented cars and their corresponding 3D models.

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Object Indentification Using 3D SketchUp Models in Environment Scans

  1. 1. Object Identification Using 3D SketchUp Models in Environment Scans Flavia Grosan, Alexandru Tandrau Prof. Dr. Andreas NüchterThursday, May 12, 2011
  2. 2. Introduction n “I can’t find my Audi A4. Bot, please find it for me!”Thursday, May 12, 2011
  3. 3. Introduction n SLAM n Laser range scanners n ICP n SemanticsThursday, May 12, 2011
  4. 4. State of the Art n Horn - closed form solution for ICP n translation, rotation, scale n Nüchter - semantic mapping n determine coarse features: walls, floors n trained classifier to identify more delicate objectsThursday, May 12, 2011
  5. 5. State of the Art n Object Localization n Li-Jia - 2D object localization n Meger - Semantic Robot Challenge n Kestler - probabilistic representation n manual labeling needed n maintains internal neural net trained data n Lai and Fox - Google Warehouse to train classifiers n Albrecht - CAD models and ontologiesThursday, May 12, 2011
  6. 6. Scientific Contribution n Combine laser scanning with object detection and localization n Simple scan matching instead of classifiers and probabilistic approaches n Evaluates Google 3D Warehouse - a new, large 3D model databaseThursday, May 12, 2011
  7. 7. From Model to Point Cloud n Google 3D Warehouse - collection of user made SketchUp models n A model is composed of: n Faces n ComponentInstances n GroupsThursday, May 12, 2011
  8. 8. From Model to Point Cloud n Additional sampling procedure needed n Add random points inside each triangular face proportionally to its area n Center the point cloud around its centroid and bound the coordinates in [-α, α]Thursday, May 12, 2011
  9. 9. Model - Scan Matching Is the model present in the scan? If so, where?Thursday, May 12, 2011
  10. 10. Model - Scan Matching n Ground Removal n Stiene et al. n Compute gradient between closest points in the same vertical sweep plane n A point is classified as ground if -θ ≤ αi,j ≤ θThursday, May 12, 2011
  11. 11. Model - Scan Matching Object segmentation by region growing with starting pointThursday, May 12, 2011
  12. 12. Model - Scan Matching Object segmentation by region growing with starting pointThursday, May 12, 2011
  13. 13. Model - Scan Matching n The centroid of the segmented object is the new system origin. The object coordinates are bounded in [-α, α] (center and scale step) n Modified ICP (SICP) to match model and scan n Scale n Favor rotations on the y-axis (wheels on the ground) n Points are linked in both directions (scan to model, model to scan) n Recover transformation matrix to original scanThursday, May 12, 2011
  14. 14. Model - Scan Matching SICP animationThursday, May 12, 2011
  15. 15. Model - Scan Matching SICP animationThursday, May 12, 2011
  16. 16. Model - Scan Matching n S - the scan points, M - the model points n c(p) ∈ M - the model point which is closest to p ∈ S the error function penalizes points in the scan which have no correspondence in the modelThursday, May 12, 2011
  17. 17. Experiments and Results n acquired scans using a Riegl VZ-400 3D laser scanner in the Jacobs University parking lot n segmented 5 different cars based on starting points n automatically downloaded relevant Google SketchUp models and pre-processed them (resampling, scale & center) n SICP with 4 starting rotations around the vertical axisThursday, May 12, 2011
  18. 18. Mercedes C350 n 8920 points in scan n 89 models available in Google WarehouseThursday, May 12, 2011
  19. 19. Mercedes C350 - best modelsThursday, May 12, 2011
  20. 20. Mercedes C350 - worst modelsThursday, May 12, 2011
  21. 21. Audi A4 n 18801 points in scan n 80 models available in Google WarehouseThursday, May 12, 2011
  22. 22. Audi A4 - best modelsThursday, May 12, 2011
  23. 23. Audi A4 - worst modelsThursday, May 12, 2011
  24. 24. Audi A4 in entire scanThursday, May 12, 2011
  25. 25. Mercedes C350 vs different brand modelsThursday, May 12, 2011
  26. 26. Conclusions n Segmented objects above 10,000 points behaved well in SICP n controlled in practice by taking more scans Perfect Match % n Number of Google Warehouse models 0 10 20 30 40 n Volkswagen Golf - 200+ models Audi A4 n Citroen C5 - 11 models VW Golf Mercedes C350 n ~ 80 models needed to get good matchesThursday, May 12, 2011
  27. 27. Conclusions n Identified Volkswagen Golf - an older variant Best matches distribution VW Golf n SICP ranks higher Google models resembling the old Golf version Newer Golf 37% Older Golf 63% n SICP identifies similar shapes n Mercedes C350 vs. other car brandsThursday, May 12, 2011
  28. 28. Conclusions n SICP solves the goal finding problem n Automatic scan segmentation n Correct identification of Audi A4 n Next 2 matches - also cars, different brandsThursday, May 12, 2011
  29. 29. Future Work n Refine model search in Google Warehouse n Improve the error function n Tackle indoor scenarios n SICP - extendable to full-scene understanding n Create an online platform for SICP n Integrate SICP as a plugin for RoboEarthThursday, May 12, 2011