Semantic mapping extension for OpenStreetMap applied to indoor robot navigation
Lakshadeep Naik, Sebastian Blumenthal, Nico Huebel, Herman Bruyninckx, Erwin Prassler
Abstract
In this work a graph-based, semantic mapping approach for indoor robotics applications is presented, which is extending OpenStreetMap (OSM) with robotic-specific, semantic, topological, and geometrical information. Models are introduced for basic indoor structures such as walls, doors, corridors, elevators, etc. The architectural principles support composition with additional domain and application-specific knowledge. As an example, a model for an area is introduced, and it is explained how this can be used in navigation. A key advantage of the proposed graph-based map representation is that it allows exploiting the hierarchical structure of the graphs. Finally, the compatibility of the approach with existing, grid-based motion planning algorithms is shown.
BibTeX
@inproceedings{icra2019_semanticmappinge,
title = {Semantic mapping extension for OpenStreetMap applied to indoor robot navigation},
author = {Lakshadeep Naik and Sebastian Blumenthal and Nico Huebel and Herman Bruyninckx and Erwin Prassler},
booktitle = {ICRA 2019},
year = {2019}
}