Semantic representation for navigation in large-scale environments
Romain Drouilly, Patrick Rives, Benoit Morisset
Abstract
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 high-level 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.
BibTeX
@inproceedings{icra2015_semanticrepresen,
title = {Semantic representation for navigation in large-scale environments},
author = {Romain Drouilly and Patrick Rives and Benoit Morisset},
booktitle = {ICRA 2015},
year = {2015}
}