An application of fuzzy DL-Based semantic perception to soil container classification
Eich, Markus (2013) An application of fuzzy DL-Based semantic perception to soil container classification. In Proceedings of 2013 IEEE International Conference on Technologies for Practical Robot Applications (TePRA), IEEE, Woburn, MA, pp. 1-8.
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.
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|Item Type:||Conference Paper|
|Keywords:||Semantic Perception, own|
|Divisions:||Current > QUT Faculties and Divisions > Science & Engineering Faculty|
|Deposited On:||10 May 2015 22:53|
|Last Modified:||19 May 2015 05:40|
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