Towards 3D Object Maps for Autonomous Household Robots (bibtex)
by Radu Bogdan Rusu, Nico Blodow, Zoltan-Csaba Marton, Alina Soos, Michael Beetz
Abstract:
This paper describes a mapping system that acquires 3D object models of man-made indoor environments such as kitchens. The system segments and geometrically reconstructs cabinets with doors, tables, drawers, and shelves, objects that are important for robots retrieving and manipulating objects in these environments. The system also acquires models of objects of daily use such glasses, plates, and ingredients. The models enable the recognition of the objects in cluttered scenes and the classification of newly encountered objects. Key technical contributions include (1) a robust, accurate, and efficient algorithm for constructing complete object models from 3D point clouds constituting partial object views, (2) feature-based recognition procedures for cabinets, tables, and other task-relevant furniture objects, and (3) automatic inference of object instance and class signatures for objects of daily use that enable robots to reliably recognize the objects in cluttered and real task contexts. We present results from the sensor-based mapping of a real kitchen.
Reference:
Radu Bogdan Rusu, Nico Blodow, Zoltan-Csaba Marton, Alina Soos, Michael Beetz, "Towards 3D Object Maps for Autonomous Household Robots", In Proceedings of the 20th IEEE International Conference on Intelligent Robots and Systems (IROS), San Diego, CA, USA, 2007.
Bibtex Entry:
@InProceedings{rusu07towards,
  author    = {Radu Bogdan Rusu and Nico Blodow and Zoltan-Csaba Marton and Alina Soos and Michael Beetz},
  title     = {Towards 3D Object Maps for Autonomous Household Robots},
  booktitle = {Proceedings of the 20th IEEE International Conference on Intelligent Robots and Systems (IROS)},
  year      = {2007},
  address   = {San Diego, CA, USA},
  abstract          = {This paper describes a mapping system that
  acquires 3D object models of man-made indoor environments such as
  kitchens. The system segments and geometrically reconstructs cabinets
  with doors, tables, drawers, and shelves, objects that are important
  for robots retrieving and manipulating objects in these environments.
  The system also acquires models of objects of daily use such glasses,
  plates, and ingredients. The models enable the recognition of the
  objects in cluttered scenes and the classification of newly
  encountered objects.

  Key technical contributions include (1)~a robust, accurate, and
  efficient algorithm for constructing complete object models from 3D
  point clouds constituting partial object views, (2)~feature-based
  recognition procedures for cabinets, tables, and other task-relevant
  furniture objects, and (3)~automatic inference of object instance and
  class signatures for objects of daily use that enable robots to reliably
  recognize the objects in cluttered and real task contexts. We present
  results from the sensor-based mapping of a real kitchen.},
  bib2html_pubtype = {Conference Paper},
  bib2html_rescat  = {Perception, Models},
  bib2html_groups  = {Cop, EnvMod},
  bib2html_funding = {CoTeSys},
  bib2html_domain  = {Assistive Household}
}
Powered by bibtexbrowser