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Bryan Willimon, Stan Birchfield, Ian Walker
Department of Electrical and Computer Engineering
Clemson UniversityIROS 2010
Rigid and Non-Rigid Classification Using Interactive
Perception
What is Interactive Perception?
Interactive Perception is the concept of gathering information about a particular object through interaction
Raccoons and cats use this technique by moving objects around using their front paws.
What is Interactive Perception?
The information gathered is either complimenting information obtained through vision or adding new information that can’t be determined through vision alone
Previous Related Work on Interactive Perception
P. Fitzpatrick. First Contact: an active vision approach to segmentation. IROS 2003
Segmentation through image differencing
Learning about prismatic and revolute joints on planar rigid objects
D. Katz and O. Brock. Manipulating articulated objects with interactive perception. ICRA 2008
Previous work focused on rigid objects
Goal of Our Approach
Isolated Object Classify Object
Learn about Object
Color Histogram Labeling Use color values (RGB) of the object to create a 3-D
histogram Each histogram is normalized by number of pixels in object
to create a probability distribution Each histogram is then compared to histograms of previous
objects for a match using histogram intersection* White area is found by using same technique as in graph-
based segmentation and used as a binary mask to locate object in image
Skeletonization
Use binary mask from previous step to create a skeleton of the object
Skeleton is a single-pixel wide outline of the area Prairie-fire analogy
Iteration 1Iteration 3Iteration 5Iteration 7Iteration 9Iteration 10Iteration 11Iteration 13Iteration 15Iteration 17Iteration 47
Monitoring Object Interaction
Use KLT feature points to track movement of the object as the robot interacts with it
Only concerned with feature points on the object and disregard all other points
Calculate distance between each feature point every flength frames (flength=5)
Monitoring Object Interaction (cont.)
Idea: Like features keep a constant inter-feature distance, features from different groups have variable intra-distance
Features were separated into groups by measuring the intra-distance amount after flength frames
If the intra-distance between two features changes by less than a threshold, then they are within the same group
Otherwise, they are within different groups Separate groups relate to
separate parts of an object
Labeling Revolute Joints using Motion
For each feature group, create an ellipse that encapsulates all features
Calculate major axis of ellipse using PCA End points of major axis correspond to a revolute joint
and the endpoint of the extremity
Labeling Revolute Joints using Motion (cont.)
Using the skeleton, locate intersection points and end points
Intersection points (Red) = Rigid or Non-rigid joints End points (Green) = Interaction points Interaction points are locations that the robot uses to
“push” or “poke” the object
Labeling Revolute Joints using Motion (cont.)
Map estimated revolute joint from major axis of ellipse to actual joint in skeleton
After multiple interactions from the robot, a final skeleton is created with revolute joints labeled (red)
Experimental Results
Sorting using socks and shoes
Articulated rigid object - pliers
Classification experiment - toys
*D. Katz and O. Brock. Manipulating articulated objects with interactive perception. ICRA 2008
Comparing objects of the same type to that of similar work* Pliers from our results compared to shears in their results*
Our approach Katz-Brock approach
Results Articulated rigid object (Pliers)
Final Skeleton used for Classification
Results Classification (cont.) Experiment
(Toys)
1 2 3 4
Results Classification (cont.) Experiment
(Toys)
5 6 7 8
Results Classification (cont.) Experiment
(Toys)
Classification Experiment without use of Skeleton
*Rows = Query image, Columns = Database image
Results Classification (cont.) Experiment
Misclassification
Classification Experiment with use of Skeleton
*Rows = Query image, Columns = Database image
Results Classification (cont.) Experiment
Classification Corrected
Results Sorting (cont.) using socks
and shoes
1 2 3 4 5
Results Sorting (cont.) using socks
and shoes
Classification Experiment without use of Skeleton
Misclassification
Results Sorting (cont.) using socks
and shoes
Classification Experiment with use of Skeleton
Classification Corrected
Conclusion
The results demonstrated that our approach provided a way to classify rigid and non-rigid objects and label them for sorting and/or pairing purposes Most of the previous work only considers planar rigid
objects This approach builds on and exceeds previous work in the
scope of “interactive perception” We gather more information with interaction like a skeleton
of the object, color, and movable joints. Other works only look to segment the object or find
revolute and prismatic joints
Future Work
Create a 3-D environment instead of a 2-D environment Modify classification area to allow for interactions from
more than 2 directions Improve the gripper of the robot for more robust grasping Enhance classification algorithm and learning strategy
Use more characteristics to properly label a wider range of objects
Questions?