The high potential of Superquadrics as modeling elements for image segmentation tasks has been pointed out since years in the computer vision community. In this work we employ superquadrics as modeling elements for multiple object segmentation in range images. Segmentation is executed in two stages. Firstly, a hypothesis about the values of the segmentation parameters is generated. Secondly, the hypothesis is refined locally. In both stages, object boundary and region information are considered. Boundary information is derived via model-based edge detection in the input range image. Hypothesis generation uses boundary information to isolate image regions which can be accurately described by superquadrics. Within hypothesis refinement, a game-theoretic framework is used to fuse the two information sources by associating an objective function to each information source. Iterative optimization of the two objective functions in succession, outputs a precise description of all image objects. We demonstrate experimentally that this approach substantially improves the most established method in superquadric segmentation, in terms of accuracy and computational efficiency. We demonstrate the applicability of our segmentation framework in real world applications by constructing a novel robotic system for automatic unloading of jumbled box-like objects from platforms.

Automatic unloading of piled box-like objects is undoubtedly of great importance to the industry. In this contribution a system addressing this problem is described: we employ a laser range finder for data acquisition, and globally deformable superquadrics for object modeling. Our technique is based on a hypothesis generation and refinement scheme. The vertices of the piled objects are extracted and superquadric seeds are aligned at these vertices. The model parameter recovery task is decomposed into two subproblems, each dealing with a subset of the model's parameter set. Both region and boundary based information sources are used for parameter estimation. Compared to a widespread strategy for superquadric recovery, our method shows advantages in terms of robustness and computational efficiency. In addition, our system exhibits versatility with regard to existing industrial systems, since it can effectively deal with both neatly placed and jumbled configurations of objects.

We present a box depalletizing system based on images acquired with a time of flight laser sensor mounted on the hand of the robot. Scanning the upper layer of the pallet yields a 2.5D image to which edge detection and robust line fitting are applied to extract 3D vertices. This vertex information is used as an input for a model based object recognition system. Model vertices are matched to scene vertices and object location hypothesis are formed. These hypotheses are verified or rejected using a two-step verification process. Due to the fact that we use edges to extract vertices, rather than surfaces, we are able to detect target objects, in both cluttered and ordered configurations. Our experiments with different configurations of cardboard boxes and paper tissue packets demonstrate the validity of our approach. The main advantages of our system are it's versatility, simplicity, efficiency and robustness.

We propose an efficient approach towards the solution of the de-palletizing problem, based on active vision. We describe a system comprising an industrial robot and a time of flight laser sensor, which performs the depalletizing task in real time, and independently of lighting conditions. In our case, the target objects are solid boxes of known identical dimensions, neatly layered but with arbitrary orientation within a layer, which are all placed on a platform. The layered structure of the target platform allows for 2D imagery. The system locates the position of the boxes by tracking one of the corners they expose to the laser source. The system locates the desired corners by applying the scan line approximation technique, adapted to fit the needs of our application, to the 2D input data. The advantages of our system over existing applications are its simplicity, robustness, speed and ease of installation.