Qualitative Map Learning Based on Co-visibility of Objects
Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence. Pages 183-189.
Abstract
This paper proposes a unique map learning method for mobile robots based on the co-visibility information of objects i.e., the information on whether two objects are visible at the same time or not from the current position. This method first estimates empirical distances among the objects using a simple heuristics - "a pair of objects observed at the same time more frequently is likely to be located more closely together". Then it computes all the coordinates of the objects by multidimensional scaling (MDS) technique. In the latter part of this paper, it is shown that the proposed method is able to learn qualitatively very accurate maps though it uses only such primitive information, and that it is robust against some kinds of object recognition errors.
@inproceedings{ijcai2003p26,
title = {Qualitative Map Learning Based on Co-visibility of Objects},
author = {Yairi, Takehisa and Hori, Koichi},
booktitle = {Proceedings of the Eighteenth International Joint Conference on
Artificial Intelligence, {IJCAI-03}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Georg Gottlob},
pages = {183--189},
year = {2003},
url = {https://ijcai.org/proceedings/2003/26},
}
TY - CPAPER TI - Qualitative Map Learning Based on Co-visibility of Objects AU - Yairi, Takehisa AU - Hori, Koichi T2 - Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence, IJCAI-03 PB - International Joint Conferences on Artificial Intelligence PY - 2003 SP - 183 EP - 189 UR - https://ijcai.org/proceedings/2003/26 ER -Copied
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