An Application of Fuzzy DL-Based Semantic Perception to Soil Container Classification

Markus Eich

In: 2013 IEEE International Conference on Technologies for Practical Robot Applications. IEEE International Conference on Technologies for Practical Robot Applications (TePRA-2013) April 22-23 Woburn Masachusetts United States 4/2013.


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 fetch and- 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.


German Research Center for Artificial Intelligence
Deutsches Forschungszentrum für Künstliche Intelligenz