You are here: Home → Proceedings
Document Actions

Citation and metadata

Back to the article.

Recommended citation

Knitt M, Elgouhary Y, Schyga J, Rose H, Braun P, Kreutzfeldt J (2023). Benchmarking for the Indoor Localization of Autonomous Mobile Robots in Intralogistics. Logistics Journal : Proceedings, Vol. 2023. (urn:nbn:de:0009-14-58098)

Download Citation

Endnote

%0 Journal Article
%T Benchmarking for the Indoor Localization of Autonomous Mobile Robots in Intralogistics
%A Knitt, Markus
%A Elgouhary, Yousef
%A Schyga, Jakob
%A Rose, Hendrik
%A Braun, Philipp
%A Kreutzfeldt, Jochen
%J Logistics Journal : Proceedings
%D 2023
%V 2023
%N 1
%@ 2192-9084
%F knitt2023
%X This paper introduces a novel approach to benchmarking Indoor Localization Systems (ILS) for mobile robots in warehouse and manufacturing contexts. The study focuses on diverse localization technologies commonly used in mobile robotics and implements transparent and comparable performance metrics, an automated experimental procedure, as well as an intuitive performance visualization approach. Experiments were conducted using a custom-built robot equipped with various sensors, including LiDAR, Ultra-Wideband (UWB), and vision systems. A process for systematically analyzing the impact of environmental factors such as lighting, reflectivity, and obstacles on localization performance is proposed. The results provide insights into system robustness and accuracy under different conditions. The study enables more efficient experimental analysis of sensor fusion and optimization strategies for achieving optimal performance and offers a workflow to efficiently investigate sensor fusion concepts using real data.
%L 620
%K Benchmarking
%K Lokalisierung
%K Robotik
%K Intralogistik
%K Localization
%K Robotics
%K Intralogistics
%R 10.2195/lj_proc_knitt_de_202310_01
%U http://nbn-resolving.de/urn:nbn:de:0009-14-58098
%U http://dx.doi.org/10.2195/lj_proc_knitt_de_202310_01
Download

Bibtex

@Article{knitt2023,
  author = 	"Knitt, Markus
		and Elgouhary, Yousef
		and Schyga, Jakob
		and Rose, Hendrik
		and Braun, Philipp
		and Kreutzfeldt, Jochen",
  title = 	"Benchmarking for the Indoor Localization of Autonomous Mobile Robots in Intralogistics",
  journal = 	"Logistics Journal : Proceedings",
  year = 	"2023",
  volume = 	"2023",
  number = 	"1",
  keywords = 	"Benchmarking; Lokalisierung; Robotik; Intralogistik; Localization; Robotics; Intralogistics",
  abstract = 	"This paper introduces a novel approach to benchmarking Indoor Localization Systems (ILS) for mobile robots in warehouse and manufacturing contexts. The study focuses on diverse localization technologies commonly used in mobile robotics and implements transparent and comparable performance metrics, an automated experimental procedure, as well as an intuitive performance visualization approach. Experiments were conducted using a custom-built robot equipped with various sensors, including LiDAR, Ultra-Wideband (UWB), and vision systems. A process for systematically analyzing the impact of environmental factors such as lighting, reflectivity, and obstacles on localization performance is proposed. The results provide insights into system robustness and accuracy under different conditions. The study enables more efficient experimental analysis of sensor fusion and optimization strategies for achieving optimal performance and offers a workflow to efficiently investigate sensor fusion concepts using real data.",
  issn = 	"2192-9084",
  doi = 	"10.2195/lj_proc_knitt_de_202310_01",
  url = 	"http://nbn-resolving.de/urn:nbn:de:0009-14-58098"
}
Download

RIS

TY  - JOUR
AU  - Knitt, Markus
AU  - Elgouhary, Yousef
AU  - Schyga, Jakob
AU  - Rose, Hendrik
AU  - Braun, Philipp
AU  - Kreutzfeldt, Jochen
PY  - 2023
DA  - 2023//
TI  - Benchmarking for the Indoor Localization of Autonomous Mobile Robots in Intralogistics
JO  - Logistics Journal : Proceedings
VL  - 2023
IS  - 1
KW  - Benchmarking
KW  - Lokalisierung
KW  - Robotik
KW  - Intralogistik
KW  - Localization
KW  - Robotics
KW  - Intralogistics
AB  - This paper introduces a novel approach to benchmarking Indoor Localization Systems (ILS) for mobile robots in warehouse and manufacturing contexts. The study focuses on diverse localization technologies commonly used in mobile robotics and implements transparent and comparable performance metrics, an automated experimental procedure, as well as an intuitive performance visualization approach. Experiments were conducted using a custom-built robot equipped with various sensors, including LiDAR, Ultra-Wideband (UWB), and vision systems. A process for systematically analyzing the impact of environmental factors such as lighting, reflectivity, and obstacles on localization performance is proposed. The results provide insights into system robustness and accuracy under different conditions. The study enables more efficient experimental analysis of sensor fusion and optimization strategies for achieving optimal performance and offers a workflow to efficiently investigate sensor fusion concepts using real data.
SN  - 2192-9084
UR  - http://nbn-resolving.de/urn:nbn:de:0009-14-58098
DO  - 10.2195/lj_proc_knitt_de_202310_01
ID  - knitt2023
ER  -
Download

Wordbib

<?xml version="1.0" encoding="UTF-8"?>
<b:Sources SelectedStyle="" xmlns:b="http://schemas.openxmlformats.org/officeDocument/2006/bibliography"  xmlns="http://schemas.openxmlformats.org/officeDocument/2006/bibliography" >
<b:Source>
<b:Tag>knitt2023</b:Tag>
<b:SourceType>ArticleInAPeriodical</b:SourceType>
<b:Year>2023</b:Year>
<b:PeriodicalTitle>Logistics Journal : Proceedings</b:PeriodicalTitle>
<b:Volume>2023</b:Volume>
<b:Issue>1</b:Issue>
<b:Url>http://nbn-resolving.de/urn:nbn:de:0009-14-58098</b:Url>
<b:Url>http://dx.doi.org/10.2195/lj_proc_knitt_de_202310_01</b:Url>
<b:Author>
<b:Author><b:NameList>
<b:Person><b:Last>Knitt</b:Last><b:First>Markus</b:First></b:Person>
<b:Person><b:Last>Elgouhary</b:Last><b:First>Yousef</b:First></b:Person>
<b:Person><b:Last>Schyga</b:Last><b:First>Jakob</b:First></b:Person>
<b:Person><b:Last>Rose</b:Last><b:First>Hendrik</b:First></b:Person>
<b:Person><b:Last>Braun</b:Last><b:First>Philipp</b:First></b:Person>
<b:Person><b:Last>Kreutzfeldt</b:Last><b:First>Jochen</b:First></b:Person>
</b:NameList></b:Author>
</b:Author>
<b:Title>Benchmarking for the Indoor Localization of Autonomous Mobile Robots in Intralogistics</b:Title>
<b:Comments>This paper introduces a novel approach to benchmarking Indoor Localization Systems (ILS) for mobile robots in warehouse and manufacturing contexts. The study focuses on diverse localization technologies commonly used in mobile robotics and implements transparent and comparable performance metrics, an automated experimental procedure, as well as an intuitive performance visualization approach. Experiments were conducted using a custom-built robot equipped with various sensors, including LiDAR, Ultra-Wideband (UWB), and vision systems. A process for systematically analyzing the impact of environmental factors such as lighting, reflectivity, and obstacles on localization performance is proposed. The results provide insights into system robustness and accuracy under different conditions. The study enables more efficient experimental analysis of sensor fusion and optimization strategies for achieving optimal performance and offers a workflow to efficiently investigate sensor fusion concepts using real data.</b:Comments>
</b:Source>
</b:Sources>
Download

ISI

PT Journal
AU Knitt, M
   Elgouhary, Y
   Schyga, J
   Rose, H
   Braun, P
   Kreutzfeldt, J
TI Benchmarking for the Indoor Localization of Autonomous Mobile Robots in Intralogistics
SO Logistics Journal : Proceedings
PY 2023
VL 2023
IS 1
DI 10.2195/lj_proc_knitt_de_202310_01
DE Benchmarking; Lokalisierung; Robotik; Intralogistik; Localization; Robotics; Intralogistics
AB This paper introduces a novel approach to benchmarking Indoor Localization Systems (ILS) for mobile robots in warehouse and manufacturing contexts. The study focuses on diverse localization technologies commonly used in mobile robotics and implements transparent and comparable performance metrics, an automated experimental procedure, as well as an intuitive performance visualization approach. Experiments were conducted using a custom-built robot equipped with various sensors, including LiDAR, Ultra-Wideband (UWB), and vision systems. A process for systematically analyzing the impact of environmental factors such as lighting, reflectivity, and obstacles on localization performance is proposed. The results provide insights into system robustness and accuracy under different conditions. The study enables more efficient experimental analysis of sensor fusion and optimization strategies for achieving optimal performance and offers a workflow to efficiently investigate sensor fusion concepts using real data.
ER
Download

Mods

<mods>
  <titleInfo>
    <title>Benchmarking for the Indoor Localization of Autonomous Mobile Robots in Intralogistics</title>
  </titleInfo>
  <name type="personal">
    <namePart type="family">Knitt</namePart>
    <namePart type="given">Markus</namePart>
  </name>
  <name type="personal">
    <namePart type="family">Elgouhary</namePart>
    <namePart type="given">Yousef</namePart>
  </name>
  <name type="personal">
    <namePart type="family">Schyga</namePart>
    <namePart type="given">Jakob</namePart>
  </name>
  <name type="personal">
    <namePart type="family">Rose</namePart>
    <namePart type="given">Hendrik</namePart>
  </name>
  <name type="personal">
    <namePart type="family">Braun</namePart>
    <namePart type="given">Philipp</namePart>
  </name>
  <name type="personal">
    <namePart type="family">Kreutzfeldt</namePart>
    <namePart type="given">Jochen</namePart>
  </name>
  <abstract>This paper introduces a novel approach to benchmarking Indoor Localization Systems (ILS) for mobile robots in warehouse and manufacturing contexts. The study focuses on diverse localization technologies commonly used in mobile robotics and implements transparent and comparable performance metrics, an automated experimental procedure, as well as an intuitive performance visualization approach. Experiments were conducted using a custom-built robot equipped with various sensors, including LiDAR, Ultra-Wideband (UWB), and vision systems. A process for systematically analyzing the impact of environmental factors such as lighting, reflectivity, and obstacles on localization performance is proposed. The results provide insights into system robustness and accuracy under different conditions. The study enables more efficient experimental analysis of sensor fusion and optimization strategies for achieving optimal performance and offers a workflow to efficiently investigate sensor fusion concepts using real data.</abstract>
  <subject>
    <topic>Benchmarking</topic>
    <topic>Lokalisierung</topic>
    <topic>Robotik</topic>
    <topic>Intralogistik</topic>
    <topic>Benchmarking</topic>
    <topic>Localization</topic>
    <topic>Robotics</topic>
    <topic>Intralogistics</topic>
  </subject>
  <classification authority="ddc">620</classification>
  <relatedItem type="host">
    <genre authority="marcgt">periodical</genre>
    <genre>academic journal</genre>
    <titleInfo>
      <title>Logistics Journal : Proceedings</title>
    </titleInfo>
    <part>
      <detail type="volume">
        <number>2023</number>
      </detail>
      <detail type="issue">
        <number>1</number>
      </detail>
      <date>2023</date>
    </part>
  </relatedItem>
  <identifier type="issn">2192-9084</identifier>
  <identifier type="urn">urn:nbn:de:0009-14-58098</identifier>
  <identifier type="doi">10.2195/lj_proc_knitt_de_202310_01</identifier>
  <identifier type="uri">http://nbn-resolving.de/urn:nbn:de:0009-14-58098</identifier>
  <identifier type="citekey">knitt2023</identifier>
</mods>
Download

Full Metadata

Bibliographic Citation Logistics Journal : referierte Veröffentlichungen, Vol. 2023, Iss. 1
Title

Benchmarking for the Indoor Localization of Autonomous Mobile Robots in Intralogistics (eng)

Benchmarking für die Indoor-Lokalisierung Autonomer Mobiler Roboter in der Intralogistik (ger)
Author Markus Knitt, Yousef Elgouhary, Jakob Schyga, Hendrik Rose, Philipp Braun, Jochen Kreutzfeldt
Language eng
Abstract This paper introduces a novel approach to benchmarking Indoor Localization Systems (ILS) for mobile robots in warehouse and manufacturing contexts. The study focuses on diverse localization technologies commonly used in mobile robotics and implements transparent and comparable performance metrics, an automated experimental procedure, as well as an intuitive performance visualization approach. Experiments were conducted using a custom-built robot equipped with various sensors, including LiDAR, Ultra-Wideband (UWB), and vision systems. A process for systematically analyzing the impact of environmental factors such as lighting, reflectivity, and obstacles on localization performance is proposed. The results provide insights into system robustness and accuracy under different conditions. The study enables more efficient experimental analysis of sensor fusion and optimization strategies for achieving optimal performance and offers a workflow to efficiently investigate sensor fusion concepts using real data. In diesem Beitrag wird ein neuartiger Ansatz zum Benchmarking von Indoor-Lokalisierungssystemen (ILS) für mobile Roboter in Lager- und Produktion-sumgebungen vorgestellt. Die Studie konzentriert sich auf verschiedene Lokalisierungstechnologien, die üblicherweise in der mobilen Robotik verwendet werden, und implementiert transparente und vergleichbare Leistungsmetriken, ein automatisiertes Experimentierverfahren und einen intuitiven Ansatz zur Leistungsvisualisierung. Die Experimente wurden mit einem speziell angefertigten Roboter durchgeführt, der mit verschiedenen Sensoren ausgestattet war, darunter LiDAR-, UWB- und Vision-Systeme. Es wird eine Methode vorgeschlagen, um die Auswirkungen von Umgebungsfaktoren wie Beleuchtung, Reflektivität und Hindernisse auf die Lokalisierungsleistung systematisch zu analysieren. Die Ergebnisse geben Aufschluss über die Robustheit und Genauigkeit des Systems unter verschiedenen Bedingungen. Die Studie ermöglicht eine effizientere experimentelle Analyse von Sensorfusions- und Optimierungsstrategien, um eine optimale Leistung zu erzielen, und bietet einen Arbeitsablauf für die effiziente Untersuchung von Sensorfusionskonzepten anhand realer Daten.
Subject Benchmarking, Lokalisierung, Robotik, Intralogistik, Benchmarking, Localization, Robotics, Intralogistics
DDC 620
Rights cc-by
URN: urn:nbn:de:0009-14-58098
DOI https://doi.org/10.2195/lj_proc_knitt_de_202310_01