Feature Based Occupancy Grid Maps for Sonar Based Safe-Mapping
Proceedings of the Twentieth International Joint Conference on Artificial Intelligence. Pages 2172--1.
Abstract
This paper presents a methodology for integrating features within the occupancy grid (OG) framework. The OG maps provide a dense representation of the environment. In particular they give information for every range measurement projected onto a grid. However independence assumptions between cells during updates as well as not considering sonar models lead to inconsistent maps, which may also lead the robot to take some decisions which may be unsafe or which may introduce an unnecessary overhead of run-time collision avoidance behaviors. Feature based maps provide more consistent representation by implicitly considering correlation between cells. But they are sparse due to sparseness of features in a typical environment. This paper provides a method for integrating feature based representations within the standard Bayesian framework of OG and provides a dense, more accurate and safe representation than standard OG methods.
@inproceedings{ijcai2007p350,
title = {Feature Based Occupancy Grid Maps for Sonar Based Safe-Mapping},
author = {Pandey, Amit Kumar and Krishna, K. Madhava and Nath, Mainak},
booktitle = {Proceedings of the Twentieth International Joint Conference on
Artificial Intelligence, {IJCAI-07}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Ramon López de Mántaras},
pages = {2172---1},
year = {2007},
url = {https://ijcai.org/proceedings/2007/350},
}
TY - CPAPER TI - Feature Based Occupancy Grid Maps for Sonar Based Safe-Mapping AU - Pandey, Amit Kumar AU - Krishna, K. Madhava AU - Nath, Mainak T2 - Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, IJCAI-07 PB - International Joint Conferences on Artificial Intelligence PY - 2007 SP - 2172 EP - -1 UR - https://ijcai.org/proceedings/2007/350 ER -Copied
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