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“Online and Consistent Occupancy Grid Mapping for Planning in Unknown Environments” by P. Sodhi, B. Ho, and M. Kaess. In Proc. IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems, IROS, (Macao), Nov. 2019, pp. 7879-7886.
Actively exploring and mapping an unknown environment requires integration of both simultaneous localization and mapping (SLAM) and path planning methods. Path planning relies on a map that contains free and occupied space information and is efficient to query, while the role of SLAM is to keep the map consistent as new measurements are continuously added. A key challenge, however, lies in ensuring a map representation compatible with both these objectives: that is, a map that maintains free space information for planning but can also adapt efficiently to dynamically changing pose estimates from a graph-based SLAM system. In this paper, we propose an online global occupancy map that can be corrected for accumulated drift efficiently based on incremental solutions from a sparse graph-based SLAM optimization. Our map maintains free space information for real-time path planning while undergoing a bounded number of updates in each loop closure iteration. We evaluate performance for both simulated and real-world datasets for an application involving underwater exploration and mapping.
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BibTeX entry:
@inproceedings{Sodhi19iros,
author = {P. Sodhi and B. Ho and M. Kaess},
title = {Online and Consistent Occupancy Grid Mapping for Planning in
Unknown Environments},
booktitle = {Proc. IEEE/RSJ Intl. Conf. on Intelligent Robots and
Systems, IROS},
pages = {7879-7886},
address = {Macao},
month = nov,
year = {2019}
}