Download - Object Indentification Using 3D SketchUp Models in Environment Scans

Object Identification Using 3D SketchUp Models in

Environment Scans

Flavia Grosan, Alexandru TandrauProf. Dr. Andreas Nüchter

Thursday, May 12, 2011

Introductionn “I can’t find my Audi A4. Bot, please find it for me!”


Introduction

n SLAM

n Laser range scanners

n ICP

n Semantics


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


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

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


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


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


Model - Scan Matching

Is the model present in the scan? If so, where?Thursday, May 12, 2011

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



Object segmentation by region growingwith starting point


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




SICP animationThursday, May 12, 2011


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


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


Mercedes C350

n 8920 points in scan

n 89 models available in Google Warehouse


Mercedes C350 - best models


Mercedes C350 - worst models


Audi A4

n 18801 points in scan

n 80 models available in Google Warehouse


Audi A4 - best models


Audi A4 - worst models


Audi A4 in entire scan


Mercedes C350 vs different brand models


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 %


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


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


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
