GLFP: Global Localization from a Floor Plan

Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2019

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Abstract

In this paper, we describe a method for global localization in a previously unvisited environment using only a schematic floor plan as a prior map. The floor plan need not be a precision map – it can be the sort of image found in buildings to guide people or aid evacuation. The core idea is to identify features that are stable across both a drawn floor plan and robot point-of-view LIDAR data, for example wall intersections, which appear as corners from overhead and as vertical lines from the ground. We introduce a factor graph-based global localization method that uses these features as landmarks. The detections of such descriptorless features are noisy and often ambiguous. We therefore propose robust data association based on a pairwise measurement consistency check and max- mixtures error model. We evaluate the resulting system in a real-world indoor environment, demonstrating performance comparable to a baseline system that uses a conventional LIDAR-based prior map.


bibtex

@inproceedings{wang2019iros,
    TITLE      = {{GLFP}: Global Localization from a Floor Plan},
    AUTHOR     = {Xipeng Wang and Ryan J. Marcotte and Edwin Olson},
    BOOKTITLE  = {Proceedings of the {IEEE/RSJ} International Conference on Intelligent
                 Robots and Systems {(IROS)}},
    YEAR       = {2019},
    KEYWORDS   = {Localization},
}