Semantic representation for navigation in large-scale environments

Romain Drouilly, Patrick Rives, Benoit Morisset
<span title="">2015</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/nytnrt3gtzbixld5r6a4talbky" style="color: black;">2015 IEEE International Conference on Robotics and Automation (ICRA)</a> </i> &nbsp;
Mimicking human navigation is a challenging goal for autonomous robots. This requires to explicitly take into account not only geometric representation but also high-level interpretation of the environment. In this paper, we demonstrate the capability to infer a route in a global map by using semantics. Our approach relies on an object-based representation of the world automatically built by robots from spherical images. In addition, we propose a new approach to specify paths in terms of
more &raquo; ... vel robot actions. This path description provides robots with the ability to interact with humans in an intuitive way. We perform experiments on simulated and real-world data, demonstrating the ability of our approach to deal with complex large-scale outdoor environments whilst dealing with labelling errors.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/icra.2015.7139314">doi:10.1109/icra.2015.7139314</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/icra/DrouillyRM15.html">dblp:conf/icra/DrouillyRM15</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/xffyue3osvdvtorv5vz6nkdhmm">fatcat:xffyue3osvdvtorv5vz6nkdhmm</a> </span>
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