InforLorV4, Hal, Corpus, bibRecord, 003D12

Probabilistic sensor data processing for robot localization on load-sensing floors

Identifieur interne : 003D12 ( Hal/Corpus ); précédent : 003D11; suivant : 003D13

Probabilistic sensor data processing for robot localization on load-sensing floors

Auteurs : Maxime Rio ; Francis Colas ; Mihai Andries ; François Charpillet

Source :

RBID : Hal:hal-01274696

Abstract

Load-sensing floors are capable of tracking objects without suffering from occlusions nor posing the same privacy issues as cameras. They have been mostly used to analyze human gait as a way of continuous diagnosis but could also be placed alongside robots to help monitoring in specialized institutions, such as elderly care facilities. However, large-scale deployments necessitate cheap sensors which do not necessarily offer the same precision. With more noisy sensors, lighter robots might be difficult to track and precisely localize. In this article, we investigate various models in order to estimate the position of a robot. We experiment with several robots of different weights and compare the models' estimates against ground truth measurements provided by a motion capture system. We show that with standard-sized tiles of 60 cm, we can track even the lighter robots with less than 4 cm of error.

Url:

https://hal.inria.fr/hal-01274696

Links to Exploration step

Hal:hal-01274696

Le document en format XML

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<front><div type="abstract" xml:lang="en">Load-sensing floors are capable of tracking objects without suffering from occlusions nor posing the same privacy issues as cameras. They have been mostly used to analyze human gait as a way of continuous diagnosis but could also be placed alongside robots to help monitoring in specialized institutions, such as elderly care facilities. However, large-scale deployments necessitate cheap sensors which do not necessarily offer the same precision. With more noisy sensors, lighter robots might be difficult to track and precisely localize. In this article, we investigate various models in order to estimate the position of a robot. We experiment with several robots of different weights and compare the models' estimates against ground truth measurements provided by a motion capture system. We show that with standard-sized tiles of 60 cm, we can track even the lighter robots with less than 4 cm of error.</div>
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<abstract xml:lang="en">Load-sensing floors are capable of tracking objects without suffering from occlusions nor posing the same privacy issues as cameras. They have been mostly used to analyze human gait as a way of continuous diagnosis but could also be placed alongside robots to help monitoring in specialized institutions, such as elderly care facilities. However, large-scale deployments necessitate cheap sensors which do not necessarily offer the same precision. With more noisy sensors, lighter robots might be difficult to track and precisely localize. In this article, we investigate various models in order to estimate the position of a robot. We experiment with several robots of different weights and compare the models' estimates against ground truth measurements provided by a motion capture system. We show that with standard-sized tiles of 60 cm, we can track even the lighter robots with less than 4 cm of error.</abstract>
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Probabilistic sensor data processing for robot localization on load-sensing floors

Probabilistic sensor data processing for robot localization on load-sensing floors

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