Title | ||
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An application of fuzzy DL-based semantic perception to soil container classification |
Abstract | ||
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Semantic perception and object labeling are key requirements for robots interacting with objects on a higher level. Symbolic annotation of objects allows the usage of planning algorithms for object interaction, for instance in a typical fetchand-carry scenario. In current research, perception is usually based on 3D scene reconstruction and geometric model matching, where trained features are matched with a 3D sample point cloud. In this work we propose a semantic perception method which is based on spatio-semantic features. These features are defined in a natural, symbolic way, such as geometry and spatial relation. In contrast to point-based model matching methods, a spatial ontology is used where objects are rather described how they "look like", similar to how a human would described unknown objects to another person. A fuzzy based reasoning approach matches perceivable features with a spatial ontology of the objects. The approach provides a method which is able to deal with senor noise and occlusions. Another advantage is that no training phase is needed in order to learn object features. The use-case of the proposed method is the detection of soil sample containers in an outdoor environment which have to be collected by a mobile robot. The approach is verified using real world experiments. |
Year | DOI | Venue |
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2013 | 10.1109/TePRA.2013.6556369 | Technologies for Practical Robot Applications |
Keywords | Field | DocType |
feature extraction,fuzzy reasoning,fuzzy set theory,image matching,learning (artificial intelligence),mobile robots,object recognition,ontologies (artificial intelligence),robot vision,stereo image processing,3d sample point cloud,3d scene reconstruction,feature matching,fetch-and-carry scenario,fuzzy dl-based semantic perception,fuzzy based reasoning approach,geometric model matching,mobile robot,object feature learning,object labeling,object spatial ontology,object symbolic annotation,occlusion,outdoor environment,robot-object interaction,semantic perception method,sensor noise,soil container classification,spatial relation,spatio-semantic feature,robots,learning artificial intelligence | Spatial relation,Ontology,Computer vision,Pattern recognition,Computer science,Fuzzy logic,Feature extraction,Fuzzy set,Artificial intelligence,Point cloud,Mobile robot,Cognitive neuroscience of visual object recognition | Conference |
ISSN | ISBN | Citations |
2325-0526 | 978-1-4673-6223-8 | 0 |
PageRank | References | Authors |
0.34 | 0 | 1 |
Name | Order | Citations | PageRank |
---|---|---|---|
Markus Eich | 1 | 0 | 0.34 |