Probabilistic multi-class segmentation for the Amazon Picking Challenge
| Autor: | Sebastian Höfer, Oliver Brock, Rico Jonschkowski, Roberto Martín-Martín, Clemens Eppner |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
0209 industrial biotechnology
Computer science Segmentation-based object categorization ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Probabilistic logic Scale-space segmentation 02 engineering and technology Image segmentation computer.software_genre 020901 industrial engineering & automation Robustness (computer science) Histogram 0202 electrical engineering electronic engineering information engineering RGB color model 020201 artificial intelligence & image processing Segmentation Data mining ddc:600 computer |
| Zdroj: | IROS 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) |
| Popis: | We present a method for multi-class segmentation from RGB-D data in a realistic warehouse picking setting. The method computes pixel-wise probabilities and combines them to find a coherent object segmentation. It reliably segments objects in cluttered scenarios, even when objects are translucent, reflective, highly deformable, have fuzzy surfaces, or consist of loosely coupled components. The robust performance results from the exploitation of problem structure inherent to the warehouse setting. The proposed method proved its capabilities as part of our winning entry to the 2015 Amazon Picking Challenge. We present a detailed experimental analysis of the contribution of different information sources, compare our method to standard segmentation techniques, and assess possible extensions that further enhance the algorithm’s capabilities. We release our software and data sets as open source. |
| Databáze: | OpenAIRE |
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