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Localization of Piled Boxes by Means of the Hough Transform

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##### Localization of Piled Boxes by Means of the Hough Transform

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**Localization of Piled Boxes by Means of the Hough Transform**Dimitrios Katsoulas Institute for Pattern Recognition and Image Processing University of Freiburg**Introduction**• Depalletizing: automatic unloading of piled objects via a robot. • How important a solution to the problem is? • We deal with objects most frequently encountered: Boxes, box-like objects (e.g. sacks full of material) • Applications: Post, distribution centers, airports. • Here: Boxes of unknown dimensions.**Existing Systems**• Intensity cameras are utilized. • Based on detection of markers on the exposed surfaces of the objects. • Advantages: Computational efficiency • Disadvantages: • Markers do not always exist! • Systems deal only with neatly placed configurations of objects of the same dimensions. • Performance depends on lighting conditions at installation sites.**Our approach**• Question: How can we recover the fully exposed surfaces (graspable surfaces) of the objects from input range images?**Recovery of graspable surfaces**• Fully exposed object surfaces: Planar surfaces with rectangle boundaries. • Hypothesis generation (Edge based): Recover rectangle boundaries of the graspable surfaces. • Hypothesis verification (Region based): Examine if the range points inside the hypothesized boundaries lie on the plane defined by the boundary.**Hypothesis generation**• Rectangle boundaries: Geometric parametric entities with 8 parameters: (6 pose parameters and 2 dimension parameters). • Tool for recovery of multiple parametric entities from images: Hough transform (HT). • Problems of HT: Computationally inefficient, memory consuming, not robust. • Proposed solution: Decompose the problem into simpler sub-problems, use the HT to solve each: • Recovery of the pose. • Recovery of the dimensions.**Pose recovery**• [Chen.Kak:89]: A visible vertex of a convex object provides the strongest constraints for accurately determining its pose. • Vertex detection technique: • Detect 3D object boundary lines via HT. • Group orthogonal pairs of lines to vertices. • Vertex representation: • Two boundary lines joining at the vertex point. • The intersection point of those boundaries. • Pose estimation: via alignment.**Line detection in Range Images**• Perform edge detection on input range image. • Find 3D lines in the edge map by problem decomposition: • Recover the 4 line parameters by solving 2 2D subproblems each recovering 2 line parameters Computational efficiency. • Constrain the Hough transform to lie on an 1D curve Robustness, low memory consumption.**Problems of vertex detection**• Not all the linear boundaries and as consequence not all the vertices of the exposed surfaces were recovered! • Reason: Lines passing from randomly selected points (distinguished points) are recovered computational efficiency. • Disadvantages: Some of the boundary lines are not recovered. • Side effect: Object dimensions cannot be derived from vertices only. • Solution: Derive dimensions from boundary points.**Recovery of dimensions (1)**• Dimensions: Determined from the pose parameters + the edge points. • Candidate edge points: • On the same plane with a detected vertex. • On the first quadrant of the coordinate system defined by the vertex.**Verification**• Until now we have managed to recover rectangle boundaries from images. • Question: Do those boundaries correspond to boundaries of graspable surfaces? • Answer: Derived from the range points inside the boundaries. • Verification: Check if the range points inside a recovered boundary belong to the plane defined by the boundary.**Statistical tests: Avoid thresholds.**• Multiple decisions which have to be based on thresholds: • Group lines forming an angle of 90 degrees. • Determine if image points belong to a given plane. • Thresholds: difficult to set, depends on the application and on the uncertainty in calculation of line parameters. • Can we avoid multiple thresholds? Yes, by introducing statistical tests. All thresholds are replaced by a unique significance value. • We adopt the framework of [Foerstner et.al:00] for its compactness and straightforwardness.**Experimental results**• Computational efficiency(on a Pentium 3 600 MHz): • Scanning: 6.5 sec • Edge detection: 2 sec • Hypothesis generation + verification: 8 sec. • Overall: ~ 17 sec. • Accuracy: • < 2.5 cm translational accuracy. • < 2degrees rotational accuracy. • Robustness: • No false alarms. • The system only occasionally fails to recover all the graspable surfaces in the pile.**Conclusions**• Advantages: • Insensitivity to lighting conditions: Usage of laser sensor. • Accuracy: Accurate calculation of pose parameters. • Robustness: Decisions based both on edge and region based information, statistical tests are employed. • Computational efficiency: Parameter recovery problem decomposition into smaller sub-problems. • Versatility: Deals with both jumbled and neat object configurations. • Simplicity. • Problems: • Height of objects not recovered. • System fails when no boundary information can be recovered, that is when the distance between neighbouring objects is smaller than the sensor resolution.**Do you want to know how we deal with those objects?...**• Come to ICCV 03!