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Object Identification Using 3D SketchUp Models in
Environment Scans
Flavia Grosan, Alexandru TandrauProf. Dr. Andreas Nüchter
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Introductionn “I can’t find my Audi A4. Bot, please find it for me!”
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Introduction
n SLAM
n Laser range scanners
n ICP
n Semantics
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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 objects
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State of the Artn 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
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Scientific Contributionn 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 database
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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 Groups
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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]
From Model to Point Cloud
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Model - Scan Matching
Is the model present in the scan? If so, where?Thursday, May 12, 2011
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Model - Scan Matchingn 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 ≤ θ
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Model - Scan Matching
Object segmentation by region growingwith starting point
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Model - Scan Matching
Object segmentation by region growingwith starting point
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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 scan
Model - Scan Matching
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Model - Scan Matching
SICP animationThursday, May 12, 2011
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Model - Scan Matching
SICP animationThursday, May 12, 2011
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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 scanwhich have no correspondence in the model
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Experiments and Resultsn 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 axis
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Mercedes C350
n 8920 points in scan
n 89 models available in Google Warehouse
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Mercedes C350 - best models
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Mercedes C350 - worst models
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Audi A4
n 18801 points in scan
n 80 models available in Google Warehouse
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Audi A4 - best models
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Audi A4 - worst models
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Audi A4 in entire scan
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Mercedes C350 vs different brand models
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Conclusionsn Segmented objects above 10,000 points behaved well in SICP
n controlled in practice by taking more scans
n Number of Google Warehouse models
n Volkswagen Golf - 200+ models
n Citroen C5 - 11 models
n ~ 80 models needed to get good matches
0 10 20 30 40
Audi A4
VW Golf
Mercedes C350
Perfect Match %
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Conclusionsn Identified Volkswagen Golf - an older variant
Older Golf63%
Newer Golf37%
Best matches distribution VW Golf
n SICP ranks higher Google models resembling the old Golf version
n SICP identifies similar shapes
n Mercedes C350 vs. other car brands
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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 brands
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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 RoboEarth
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