Abstract-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.
Specially, the multi-class segmentation used in REF helps the team achieve the winning entry to the APC 2015.
Probabilistic multi-class segmentation for the Amazon Picking Challenge
2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Computer Science