Daniel Wolf

h-index32
2papers
2,797citations

2 Papers

3.9ROOct 6, 2015
Where to look first? Behaviour control for fetch-and-carry missions of service robots

Markus Bajones, Daniel Wolf, Johann Prankl et al.

This paper presents the behaviour control of a service robot for intelligent object search in a domestic environment. A major challenge in service robotics is to enable fetch-and-carry missions that are satisfying for the user in terms of efficiency and human-oriented perception. The proposed behaviour controller provides an informed intelligent search based on a semantic segmentation framework for indoor scenes and integrates it with object recognition and grasping. Instead of manually annotating search positions in the environment, the framework automatically suggests likely locations to search for an object based on contextual information, e.g. next to tables and shelves. In a preliminary set of experiments we demonstrate that this behaviour control is as efficient as using manually annotated locations. Moreover, we argue that our approach will reduce the intensity of labour associated with programming fetch-and-carry tasks for service robots and that it will be perceived as more human-oriented.

4.9CVApr 23, 2014
Find my mug: Efficient object search with a mobile robot using semantic segmentation

Daniel Wolf, Markus Bajones, Johann Prankl et al.

In this paper, we propose an efficient semantic segmentation framework for indoor scenes, tailored to the application on a mobile robot. Semantic segmentation can help robots to gain a reasonable understanding of their environment, but to reach this goal, the algorithms not only need to be accurate, but also fast and robust. Therefore, we developed an optimized 3D point cloud processing framework based on a Randomized Decision Forest, achieving competitive results at sufficiently high frame rates. We evaluate the capabilities of our method on the popular NYU depth dataset and our own data and demonstrate its feasibility by deploying it on a mobile service robot, for which we could optimize an object search procedure using our results.