Relational Object Maps for Mobile Robots
Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence. Pages 1471-1475.
Abstract
Mobile robot map building is the task of generating a model of an environment from sensor data. Most existing approaches to mobile robot mapping either build topological representations or generate accurate, metric maps of an environment. In this paper we introduce relational object maps, a novel approach to building metric maps that represent individual objects such as doors or walls. We show how to extend relational Markov networks in order to reason about a hierarchy of objects and the spatial relationships between them. Markov chain Monte Carlo is used for efficient inference and to learn the parameters of the model. We show that the spatial constraints modeled by our mapping technique yield drastic improvements for labeling line segments extracted from laser range-finders.
@inproceedings{ijcai2005p229,
title = {Relational Object Maps for Mobile Robots},
author = {Limketkai, Benson and Liao, Lin and Fox, Dieter},
booktitle = {Proceedings of the Nineteenth International Joint Conference on
Artificial Intelligence, {IJCAI-05}},
publisher = {International Joint Conferences on Artificial Intelligence},
pages = {1471--1475},
year = {2005},
url = {https://ijcai.org/proceedings/2005/229},
}
TY - CPAPER TI - Relational Object Maps for Mobile Robots AU - Limketkai, Benson AU - Liao, Lin AU - Fox, Dieter T2 - Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence, IJCAI-05 PB - International Joint Conferences on Artificial Intelligence PY - 2005 SP - 1471 EP - 1475 UR - https://ijcai.org/proceedings/2005/229 ER -Copied
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