Vision-Based Global Localization Using Ceiling Space Density
Arthur Ribacki, Vitor A. M. Jorge, Mathias Mantelli, Renan Maffei, Edson Prestes
Abstract
Service robots are becoming a reality across homes. Still, the range of applications supported by such robots remains tied to their ability to self-localize in the environment. Man-made constructions often have a documented blueprint which can be used as input information for robot localization and smart home applications. However, home environments commonly include movable objects and furniture, which can make localization a complicated task, especially for forward-facing horizontal range-finders. In this paper, we present a new and effective global localization approach for home environments which adapts the notion of free space density to a camera pointing to the ceiling. We exploit the available blueprint information, as well as evidence that ceiling vision can provide robust localization information, even in the presence of occlusions. We perform real-world experiments using a robotic vacuum cleaner equipped with an upward-facing camera in two different apartments across multiple trajectories and compare the proposed method with competing approaches. Our solution shows superior localization results using maps where neither furniture or movable objects are not modeled.
BibTeX
@inproceedings{icra2018_visionbasedgloba,
title = {Vision-Based Global Localization Using Ceiling Space Density},
author = {Arthur Ribacki and Vitor A. M. Jorge and Mathias Mantelli and Renan Maffei and Edson Prestes},
booktitle = {ICRA 2018},
year = {2018}
}