Technical University of Munich

@inproceedings{rusu_towards_2007,
 author = {RB Rusu and N Blodow and ZC Marton and A Soos and M 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.},
}