Name
Abstract | ||
|---|---|---|
This paper describes a textureless object segmentation approach for autonomous service robots acting in human living environments. The proposed system allows a robot to effectively segment textureless objects in cluttered scenes by leveraging its manipulation capabilities. In our pipeline, the cluttered scenes are first statically segmented using state-of-the-art classification algorithm and then the interactive segmentation is deployed in order to resolve this possibly ambiguous static segmentation. In the second step the RGBD (RGB + Depth) sparse features, estimated on the RGBD point cloud from the Kinect sensor, are extracted and tracked while motion is induced into a scene. Using the resulting feature poses, the features are then assigned to their corresponding objects by means of a graph-based clustering algorithm. In the final step, we reconstruct the dense models of the objects from the previously clustered sparse RGBD features. We evaluated the approach on a set of scenes which consist of various textureless flat (e.g. box-like) and round (e.g. cylinder-like) objects and the combinations thereof. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ICRA.2013.6630713 | Robotics and Automation |
Keywords | Field | DocType |
dexterous manipulators,feature extraction,graph theory,human-robot interaction,image classification,image reconstruction,image segmentation,image sensors,interactive systems,natural scenes,pattern clustering,robot vision,service robots,Kinect sensor,RGBD point cloud,RGBD sparse features,autonomous service robots,box-like objects,classification algorithm,cylinder-like objects,dense object model reconstruction,feature poses,graph-based clustering algorithm,human living environments,manipulation capabilities,round objects,statically segmented cluttered scenes,textureless flat,tracking-based interactive textureless object segmentation | Computer vision,Segmentation,Computer science,Image segmentation,Feature extraction,Artificial intelligence,RGB color model,Cluster analysis,Robot,Contextual image classification,Point cloud | Conference |
Volume | Issue | ISSN |
2013 | 1 | 1050-4729 |
ISBN | Citations | PageRank |
978-1-4673-5641-1 | 12 | 0.65 |
References | Authors | |
20 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hausman, K. | 1 | 119 | 11.92 |
| Balint-Benczedi, F. | 2 | 12 | 0.65 |
| Dejan Pangercic | 3 | 214 | 13.18 |
| Zoltan-Csaba Marton | 4 | 172 | 7.64 |