Industrial Indoor Localization: Improvement of Logistics Processes Using Location Based Services

Hesslein, Niklas; Wesselhöft, Mike; Hinckeldeyn, Johannes; Kreutzfeldt, Jochen

doi:10.1007/978-3-662-62962-8_53

Cited by 9 publications

(3 citation statements)

References 10 publications

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“…The importance of indoor localization lies in the fact that it enables businesses and organizations to better understand and optimize the movement and behavior of people and assets within their premises [12]. For example, indoor localization can help a retailer track customer movement and engagement in its store, a healthcare facility monitor the location and status of its medical equipment and staff, a transportation company optimize the delivery of packages and goods [13], and a factory automate [14] and monitor the production process [15].…”

Section: Related Workmentioning

confidence: 99%

A Performance Comparison between Different Industrial Real-Time Indoor Localization Systems for Mobile Platforms

Rebelo,

Lima,

Soares

et al. 2024

Sensors

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The flexibility and versatility associated with autonomous mobile robots (AMR) have facilitated their integration into different types of industries and tasks. However, as the main objective of their implementation on the factory floor is to optimize processes and, consequently, the time associated with them, it is necessary to take into account the environment and congestion to which they are subjected. Localization, on the shop floor and in real time, is an important requirement to optimize the AMRs’ trajectory management, thus avoiding livelocks and deadlocks during their movements in partnership with manual forklift operators and logistic trains. Threeof the most commonly used localization techniques in indoor environments (time of flight, angle of arrival, and time difference of arrival), as well as two of the most commonly used indoor localization methods in the industry (ultra-wideband, and ultrasound), are presented and compared in this paper. Furthermore, it identifies and compares three industrial indoor localization solutions: Qorvo, Eliko Kio, and Marvelmind, implemented in an industrial mobile platform, which is the main contribution of this paper. These solutions can be applied to both AMRs and other mobile platforms, such as forklifts and logistic trains. In terms of results, the Marvelmind system, which uses an ultrasound method, was the best solution.

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Section: Related Workmentioning

confidence: 99%

A Performance Comparison between Different Industrial Real-Time Indoor Localization Systems for Mobile Platforms

Rebelo,

Lima,

Soares

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…This can be achieved using various techniques such as radio frequency signals [1], acoustic signals [2], magnetic fields [3,4], and vision-based methods [5]. Recently, IL has gained a lot of attention from researchers in various industries, including healthcare [6,7], logistics [8], manufacturing [9], retail [10], security [11,12] and emergency prepardness and evacuation. The research in IL has witnessed a significant progress over the last decade.…”

Section: Preliminarymentioning

confidence: 99%

Uncovering the Potential of Indoor Localization: Role of Deep and Transfer Learning

Kerdjidj¹,

Himeur²,

Sohail³

et al. 2023

Preprint

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Indoor localization (IL) is a significant topic of study with several practical applications. The area of IL has evolved greatly in recent years due to the introduction of numerous technologies such as WiFi, Bluetooth, cameras, and other sensors. Despite the growing interest in this field, there are numerous challenges and drawbacks that must be addressed to develop more accurate and sustainable systems for IL and its real-life applications. This review study gives an in-depth look into IL, covering the most promising artificial intelligence-based and hybrid strategies that have shown excellent potential in overcoming some of the limitations of classic methods. In addition, the paper investigates the significance of high-quality datasets and evaluation metrics in the design and assessment of IL algorithms. Furthermore, this overview study emphasizes the crucial role that machine learning techniques, such as deep learning and transfer learning, play in the advancement of IL. A focus on the importance of IL and the various technologies, methods, and techniques that are being used to improve it. Finally, The survey highlights the need for continued research and development to create more accurate and scalable techniques that can be applied across a range of industries, such as evacuation-egress routes, hazard-crime detection, smart occupancy-driven energy reduction and asset tracking and management.

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“…This can be achieved using various techniques such as radio frequency signals [16], acoustic signals [17], magnetic fields [3], [18], and vision-based methods [4]. Recently, IL has gained a lot of attention from researchers in various industries, including healthcare [19], [20], logistics [21], manufacturing [22], retail [23], security [24], [25] and emergency preparedness and evacuation. The research in IL has witnessed significant progress over the last decade.…”

Section: Introductionmentioning

confidence: 99%

Uncovering the Potential of Indoor Localization: Role of Deep and Transfer Learning

Kerdjidj,

Himeur,

Sohail

et al. 2024

IEEE Access

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Indoor localization (IL) is a significant topic of study with several practical applications, particularly in the context of the Internet of Things (IoT) and smart cities. The area of IL has evolved greatly in recent years due to the introduction of numerous technologies such as WiFi, Bluetooth, cameras, and other sensors. Despite the growing interest in this field, there are numerous challenges and drawbacks that must be addressed to develop more accurate and sustainable systems for IL. This review study gives an in-depth look into IL, covering the most promising artificial intelligence-based and hybrid strategies that have shown excellent potential in overcoming some of the limitations of classic methods within IoT environments. In addition, the paper investigates the significance of high-quality datasets and evaluation metrics in the design and assessment of IL algorithms. Furthermore, this overview study emphasizes the crucial role that machine learning techniques, such as deep learning and transfer learning, play in the advancement of IL. A focus on the importance of IL and the various technologies, methods, and techniques that are being used to improve it. Finally, the survey highlights the need for continued research and development to create more accurate and scalable techniques that can be applied across a range of IoT-related industries, such as evacuationegress routes, hazard-crime detection, smart occupancy-driven energy reduction and asset tracking and management.

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Industrial Indoor Localization: Improvement of Logistics Processes Using Location Based Services

Cited by 9 publications

References 10 publications

A Performance Comparison between Different Industrial Real-Time Indoor Localization Systems for Mobile Platforms

A Performance Comparison between Different Industrial Real-Time Indoor Localization Systems for Mobile Platforms

Uncovering the Potential of Indoor Localization: Role of Deep and Transfer Learning

Uncovering the Potential of Indoor Localization: Role of Deep and Transfer Learning

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