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Off-Site Indoor Localization Competitions Based on Measured Data in a Warehouse †
The performance of indoor localization methods is highly dependent on the situations in which they are used. Various competitions on indoor localization have been held for fairly comparing the existing indoor localization methods in shared and controlled testing environments. However, it is difficul...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6412289/ https://www.ncbi.nlm.nih.gov/pubmed/30781811 http://dx.doi.org/10.3390/s19040763 |
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author | Ichikari, Ryosuke Kaji, Katsuhiko Shimomura, Ryo Kourogi, Masakatsu Okuma, Takashi Kurata, Takeshi |
author_facet | Ichikari, Ryosuke Kaji, Katsuhiko Shimomura, Ryo Kourogi, Masakatsu Okuma, Takashi Kurata, Takeshi |
author_sort | Ichikari, Ryosuke |
collection | PubMed |
description | The performance of indoor localization methods is highly dependent on the situations in which they are used. Various competitions on indoor localization have been held for fairly comparing the existing indoor localization methods in shared and controlled testing environments. However, it is difficult to evaluate the practical performance in industrial scenarios through the existing competitions. This paper introduces two indoor localization competitions, which are named the “PDR Challenge in Warehouse Picking 2017” and “xDR Challenge for Warehouse Operations 2018” for tracking workers and vehicles in a warehouse scenario. For the PDR Challenge in Warehouse Picking 2017, we conducted a unique competition based on the data measured during the actual picking operation in an actual warehouse. We term the dead-reckoning of a vehicle as vehicle dead-reckoning (VDR), and the term “xDR” is derived from pedestrian dead-reckoning (PDR) plus VDR. As a sequel competition of the PDR Challenge in Warehouse Picking 2017, the xDR Challenge for Warehouse Operations 2018 was conducted as the world’s first competition that deals with tracking forklifts by VDR with smartphones. In the paper, first, we briefly summarize the existing competitions, and clarify the characteristics of our competitions by comparing them with other competitions. Our competitions have the unique capability of evaluating the practical performance in a warehouse by using the actual measured data as the test data and applying multi-faceted evaluation metrics. As a result, we successfully organize the competitions due to the many participants from many countries. As a conclusion of the paper, we summarize the findings of the competitions. |
format | Online Article Text |
id | pubmed-6412289 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-64122892019-04-03 Off-Site Indoor Localization Competitions Based on Measured Data in a Warehouse † Ichikari, Ryosuke Kaji, Katsuhiko Shimomura, Ryo Kourogi, Masakatsu Okuma, Takashi Kurata, Takeshi Sensors (Basel) Article The performance of indoor localization methods is highly dependent on the situations in which they are used. Various competitions on indoor localization have been held for fairly comparing the existing indoor localization methods in shared and controlled testing environments. However, it is difficult to evaluate the practical performance in industrial scenarios through the existing competitions. This paper introduces two indoor localization competitions, which are named the “PDR Challenge in Warehouse Picking 2017” and “xDR Challenge for Warehouse Operations 2018” for tracking workers and vehicles in a warehouse scenario. For the PDR Challenge in Warehouse Picking 2017, we conducted a unique competition based on the data measured during the actual picking operation in an actual warehouse. We term the dead-reckoning of a vehicle as vehicle dead-reckoning (VDR), and the term “xDR” is derived from pedestrian dead-reckoning (PDR) plus VDR. As a sequel competition of the PDR Challenge in Warehouse Picking 2017, the xDR Challenge for Warehouse Operations 2018 was conducted as the world’s first competition that deals with tracking forklifts by VDR with smartphones. In the paper, first, we briefly summarize the existing competitions, and clarify the characteristics of our competitions by comparing them with other competitions. Our competitions have the unique capability of evaluating the practical performance in a warehouse by using the actual measured data as the test data and applying multi-faceted evaluation metrics. As a result, we successfully organize the competitions due to the many participants from many countries. As a conclusion of the paper, we summarize the findings of the competitions. MDPI 2019-02-13 /pmc/articles/PMC6412289/ /pubmed/30781811 http://dx.doi.org/10.3390/s19040763 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Ichikari, Ryosuke Kaji, Katsuhiko Shimomura, Ryo Kourogi, Masakatsu Okuma, Takashi Kurata, Takeshi Off-Site Indoor Localization Competitions Based on Measured Data in a Warehouse † |
title | Off-Site Indoor Localization Competitions Based on Measured Data in a Warehouse † |
title_full | Off-Site Indoor Localization Competitions Based on Measured Data in a Warehouse † |
title_fullStr | Off-Site Indoor Localization Competitions Based on Measured Data in a Warehouse † |
title_full_unstemmed | Off-Site Indoor Localization Competitions Based on Measured Data in a Warehouse † |
title_short | Off-Site Indoor Localization Competitions Based on Measured Data in a Warehouse † |
title_sort | off-site indoor localization competitions based on measured data in a warehouse † |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6412289/ https://www.ncbi.nlm.nih.gov/pubmed/30781811 http://dx.doi.org/10.3390/s19040763 |
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