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IROS
2009
IEEE

Model-based and learned semantic object labeling in 3D point cloud maps of kitchen environments

8 years 12 months ago
Model-based and learned semantic object labeling in 3D point cloud maps of kitchen environments
Abstract— We report on our experiences regarding the acquisition of hybrid Semantic 3D Object Maps for indoor household environments, in particular kitchens, out of sensed 3D point cloud data. Our proposed approach includes a processing pipeline, including geometric mapping and learning, for processing large input datasets and for extracting relevant objects useful for a personal robotic assistant to perform complex manipulation tasks. The type of objects modeled are objects which perform utilitarian functions in the environment such as kitchen appliances, cupboards, tables, and drawers. The resulted model is accurate enough to use it in physics-based simulations, where doors of 3D containers can be opened based on their hinge position. The resulted map is represented as a hybrid concept and is comprised of both the hierarchically classified objects and triangular meshes used for collision avoidance in manipulation routines.
Radu Bogdan Rusu, Zoltan Csaba Marton, Nico Blodow
Added 24 May 2010
Updated 24 May 2010
Type Conference
Year 2009
Where IROS
Authors Radu Bogdan Rusu, Zoltan Csaba Marton, Nico Blodow, Andreas Holzbach, Michael Beetz
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