Paper Info
Title
Fitting superquadrics in noisy, partial views from a low-cost RGBD sensor for recognition and localization of sacks in autonomous unloading of shipping containers
Abstract
A significant amount of cargo is transported in sacks, e.g., coffee and cacao, which are predominantly handled through manual labor, e.g., when being unloaded from shipping containers. There is hence a huge potential for automation. We present here a perception pipeline to recognize and localize sacks with a low-cost sensor. The pipeline is embedded in an industrial demonstration system for container unloading. In addition to the application background, there are two main contributions presented in this paper. First, we introduce a new numerically stable form of superquadric fitting. This is of interest for the application of superquadrics in general far beyond the concrete application scenario in this paper. Second, we introduce a fast convexity test between two neighboring patches that leads to a robust segmentation for sack/bag-recognition.
Year
DOI
Venue
2014
10.1109/CoASE.2014.6899335
Automation Science and Engineering
Keywords
Field
DocType
embedded systems,freight containers,freight handling,image colour analysis,image segmentation,image sensors,numerical stability,object recognition,unloading,autonomous shipping container unloading,bag-recognition,cargo,industrial demonstration system,low-cost RGBD sensor,noisy views,numerical stability,partial views,robust segmentation,sack localization,sack recognition,superquadrics fitting
Computer vision,Engineering drawing,Superquadrics,Artificial intelligence,Engineering
Conference
Citations 
PageRank 
References 
2
0.37
18
Authors
3
Name
Order
Citations
PageRank
Narunas Vaskevicius113911.95
Kaustubh Pathak230822.19
Andreas Birk351.49