Unsupervised object exploration using context
2014 (English)In: The 23rd IEEE International Symposium on Robot and Human Interactive Communication, 2014 RO-MAN, IEEE conference proceedings, 2014, -506 p.Conference paper (Refereed)
In order for robots to function in unstructured environments in interaction with humans, they must be able to reason about the world in a semantic meaningful way. An essential capability is to segment the world into semantic plausible object hypotheses. In this paper we propose a general framework which can be used for reasoning about objects and their functionality in manipulation activities. Our system employs a hierarchical segmentation framework that extracts object hypotheses from RGB-D video. Motivated by cognitive studies on humans, our work leverages on contextual information, e.g., that objects obey the laws of physics, to formulate object hypotheses from regions in a mathematically principled manner.
Place, publisher, year, edition, pages
IEEE conference proceedings, 2014. -506 p.
Computer Vision and Robotics (Autonomous Systems)
IdentifiersURN: urn:nbn:se:kth:diva-158006DOI: 10.1109/ROMAN.2014.6926302ISBN: 978-1-4799-6763-6OAI: oai:DiVA.org:kth-158006DiVA: diva2:773359
International Symposium on Robot and Human Interactive Communication,25-29th August, Edinburgh, Scotland, UK
Qc 201501222014-12-182014-12-182015-05-04Bibliographically approved