Detecting objects in clutter is an important capability for a household robot executing pick and place tasks in realistic settings. While approaches from 2D vision work reasonably well under certain lighting conditions and given unique textures, the development of inexpensive RGBD cameras opens the way for real-time geometric approaches that do not require templates of known objects.
This paper presents a part-graph-based hashing method for classifying objects in clutter, using an additive feature descriptor. The method is incremental, allowing easy addition of new training data without recreating the complete model, and takes advantage of the additive nature of the feature to increase efficiency. It is based on a graph representation of the scene created from considering possible groupings of over-segmented scene parts, which can in turn be used in classification. Additionally, the results over multiple segmentations can be accumulated to increase detection accuracy.
We evaluated our approach on a large RGBD dataset containing over 15000 Kinect scans of 102 objects grouped in 16 categories, which we arranged into six geometric classes. Furthermore, tests on complete cluttered scenes were performed as well, and used to showcase the importance of domain adaptation.