Box-Like Superquadric Recovery in Range Images by Fusing Region and Boundary Information.

1. Box-Like Superquadric Recovery in Range Images by Fusing Region and Boundary Information. Dimitrios Katsoulas and Dimitrios Kosmopoulos NCSR Demokritos, Institute for Informatics and Telecommunications, Computational Intelligence Laboratory 4. Our Approach • Introduction • Decompose the model parameter vector in subsets: • Problem addressed: Depalletizing, or robotic bin picking, or pick and place. • Targets: Box-like objects. • Our approach: Laser sensor for data acquisition, vacuum gripper for grasping from exposed surfaces, Superquadrics as modeling elements. • Results: a) The most established method for Superquaric segmentation is outperformed. B) A robust- real time robotic bin picking system for box like objects. • Each subset is related to a different information source in the image: Pb to boundary information, Pr to region information, Ps remains constant 2. Superquadrics as modeling elements • Each parameter vector subset in the corresponding information domain. Explicit form: • The results are fused using a game theoretic framework: Two modules are used for model recovery: • The Boundary Module iteratively fits the boundary of the exposed surface of a model to the edge map of the input image: where Undeformed SQ: and L(pk, Ib, Irk) = μ1Lb(pk, Ib) + μ2Lr(pk, Irk) Boundary of exposed surface: • The Region Module iteratively fits the model to the range points enclosed by the boundary. Parameter vector: • Fusion is achieved by an iterative process including the successive invocation of the boundary module followed by the region module, until the model parameter vector does not change significantly: • 3. Recovery of multiple superquadrics (RAS) • Based on region growing + model selection. • Problems: region overgrowing, computational inefficiency. • Computational inefficiency: Addressed via initialization of high quality seeds. • Region overgrowing: Via usage of boundary information. 5. Experiments c) RAS seeds a) Intensity Image b) Range Image d) RAS output e) Edge Image f) Edge Map • 5.2 Quantitative Results • Robustness: • 174 objects, 159 true positives, 6 • false positives, 15 false negatives • Accuracy: < 1 cm in 3D, <1 • pixels per model in 2D • Efficiency: < 15 seconds per • Model in a Pentium 1.6 MHz h) Seeds in 3D j) Fitted models in 3D g) Seeds on Image plane i) Fitted models on image plane 5.1 Qualitative Results