Off-topic for aiqbench
This story was gathered from a source we follow but falls outside AI and quantum coverage, so there is no aiqbench take for it.
Location-Based Safety Management with Wearable Seamless Indoor-Outdoor Positioning
arXiv:2610.21503v1 Announce Type: new Abstract: Reliable worker localization is essential for real-time industrial safety management, but seamless outdoor-indoor positioning remains challenging. GNSS performs well outdoors yet degrades near buildings and fails indoors, while many indoor systems require costly infrastructure or extensive calibration. This paper presents a practical safety-oriented positioning system that uses resource-constrained wearable tags and a device-cloud split architecture for continuous outdoor-indoor worker tracking. The tag integrates GNSS, IMU, barometric sensing, LTE Cat-M communication, and performs lightweight sensor fusion, while the cloud server executes map-aware fusion for continuous tracking. Developed as an operational platform, the system also includes a database, a map tool, and a dashboard that provide real-time monitoring, geofencing-based access alerts, and safety and productivity analysis. Experiments with multiple participants, each walking an approximately 1400 m outdoor-indoor route, using LTE Cat-M communication, achieve mean errors of 1.32 m indoors and 3.30 m outdoors, while maintaining low positioning latency and efficient power consumption. These results demonstrate the feasibility of wearable-based seamless positioning for scalable real-time safety management.
Abstract:Reliable worker localization is essential for real-time industrial safety management, but seamless outdoor-indoor positioning remains challenging. GNSS performs well outdoors yet degrades near buildings and fails indoors, while many indoor systems require costly infrastructure or extensive calibration. This paper presents a practical safety-oriented positioning system that uses resource-constrained wearable tags and a device-cloud split architecture for continuous outdoor-indoor worker tracking. The tag integrates GNSS, IMU, barometric sensing, LTE Cat-M communication, and performs lightweight sensor fusion, while the cloud server executes map-aware fusion for continuous tracking. Developed as an operational platform, the system also includes a database, a map tool, and a dashboard that provide real-time monitoring, geofencing-based access alerts, and safety and productivity analysis. Experiments with multiple participants, each walking an approximately 1400 m outdoor-indoor route, using LTE Cat-M communication, achieve mean errors of 1.32 m indoors and 3.30 m outdoors, while maintaining low positioning latency and efficient power consumption. These results demonstrate the feasibility of wearable-based seamless positioning for scalable real-time safety management.
Robotics (cs.RO)
arXiv:2610.21503 [cs.RO]
(or arXiv:2610.21503v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.21503 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Full text under the publisher's open licence · 257 words
Related coverage

I Did This Without Your Knowledge
LessWrong · Oct 17, 2026

The best Garmin watches of 2026: Expert tested and reviewed
ZDNet · Aug 4, 2026

The best smartwatches of 2026: Expert tested and reviewed
ZDNet · Jul 29, 2026

ZenaTech Drone as a Service Closes 26th Acquisition of an Ohio - based Land Surveying Company Licensed in Four States
insidermonkey.com · Jul 23, 2026