Object Indentification Using 3D SketchUp Models in Environment Scans
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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......

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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  • 1. Object Identification Using 3D SketchUp Models in Environment Scans Flavia Grosan, Alexandru Tandrau Prof. Dr. Andreas NüchterThursday, May 12, 2011
  • 2. Introduction n “I can’t find my Audi A4. Bot, please find it for me!”Thursday, May 12, 2011
  • 3. Introduction n SLAM n Laser range scanners n ICP n SemanticsThursday, May 12, 2011
  • 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. 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. 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. 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. 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. Model - Scan Matching Is the model present in the scan? If so, where?Thursday, May 12, 2011
  • 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. Model - Scan Matching Object segmentation by region growing with starting pointThursday, May 12, 2011
  • 12. Model - Scan Matching Object segmentation by region growing with starting pointThursday, May 12, 2011
  • 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. Model - Scan Matching SICP animationThursday, May 12, 2011
  • 15. Model - Scan Matching SICP animationThursday, May 12, 2011
  • 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. 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. Mercedes C350 n 8920 points in scan n 89 models available in Google WarehouseThursday, May 12, 2011
  • 19. Mercedes C350 - best modelsThursday, May 12, 2011
  • 20. Mercedes C350 - worst modelsThursday, May 12, 2011
  • 21. Audi A4 n 18801 points in scan n 80 models available in Google WarehouseThursday, May 12, 2011
  • 22. Audi A4 - best modelsThursday, May 12, 2011
  • 23. Audi A4 - worst modelsThursday, May 12, 2011
  • 24. Audi A4 in entire scanThursday, May 12, 2011
  • 25. Mercedes C350 vs different brand modelsThursday, May 12, 2011
  • 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. 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. 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. 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