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In this paper we present a method for approximating complete models of objects with 3D shape primitives, by exploiting common symmetries in objects of daily use. Our proposed approach reconstructs boxes and cylindrical parts of objects from sampled point cloud data, and produces CAD-like surface models needed for generating grasping strategies. To verify the results, we present a set of experimental results using real-world data-sets containing a large number of objects from different views.

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Abstract. 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.

This paper dwells upon the promising 3D technology for mobile robots and automation industry. The first part of the paper describes the design details of our own 3D Time of Flight (TOF) scanning system based on 2D laser range finder. The second part presents a specific segmentation technique for 3D outdoor urban environments by the common detection of plane models. In a few words, the technique separates the raw data into sparse and dense points, followed by the segmentation of the dense points into urban background and foreground objects. In the end we present some experimental results of real-world data-sets taken from the repository 1,2 of the Leibniz University in Hannover, Germany.