Daniel Di Marco

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The vision of the RoboEarth project is to design a knowledge-based system to provide web and cloud services that can transform a simple robot into an intelligent one. In this work, we describe the RoboEarth semantic mapping system. The semantic map is composed of: 1) an ontology to code the concepts and relations in maps and objects and 2) a SLAM map(More)
The ability of reusing existing task execution plans is an important step towards autonomous behavior. Today, the reuse of sophisticated services allowing robots to act autonomous is usually limited to identical robot platforms and to very similar application scenarios. The approach presented in this paper proposes a way to mitigate this limitation by(More)
— In this paper we explore how a visual SLAM system and a robot knowledge base can mutually benefit from each other. The object recognition and mapping methods are used for grounding abstract knowledge and for creating a semantically annotated environment map that is available for reasoning. The knowledge base allows to reason about which object types are(More)
This paper presented an approach to create 3D object models for robotic and vision applications in a fast and inexpensive way compared to established approaches. By using the RoboEarth system for storing the created object models users have world-wide access to the data and can immediately reuse a model as soon as it was created and uploaded. The approach(More)
We present an application of Hierarchical Task Network (HTN) planning to create robot execution plans, that are adapted to the environment and the robot hardware from abstract task descriptions. Our main intention is to show that different robotic platforms can make use of the same high level symbolic task description. As an off-the-shelf planning(More)
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