by RB Rusu, N Blodow, ZC Marton, A Soos and M 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:
Towards 3D Object Maps for Autonomous Household Robots (RB Rusu, N Blodow, ZC Marton, A Soos and M Beetz), In Proceedings of the 20th IEEE International Conference on Intelligent Robots and Systems (IROS), 2007.
Bibtex Entry:
@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.},
}