ArticleSensors (Basel, Switzerland)2022
Evaluation and Comparison of Ultrasonic and UWB Technology for Indoor Localization in an Industrial Environment.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed, 32 citations in OpenAlex.
- An Integration of Deep Neural Network-Based Extended Kalman Filter (DNN-EKF) Method in Ultra-Wideband (UWB) Localization for Distance Loss Optimization.Sensors (Basel, Switzerland) · 2024Article
- Automated Detection of In-Home Activities with Ultra-Wideband Sensors.Sensors (Basel, Switzerland) · 2024Article
- Comparative Analysis of Integrated Filtering Methods Using UWB Localization in Indoor Environment.Sensors (Basel, Switzerland) · 2024Article
- ECG-Based Stress Detection and Productivity Factors Monitoring: The Real-Time Production Factory System.Sensors (Basel, Switzerland) · 2023Article
- Using Worker Position Data for Human-Driven Decision Support in Labour-Intensive Manufacturing.Sensors (Basel, Switzerland) · 2023Article
- A Hybrid Indoor Altimetry Based on Barometer and UWB.Sensors (Basel, Switzerland) · 2023Article
- Range-Extension Algorithms and Strategies for TDOA Ultra-Wideband Positioning System.Sensors (Basel, Switzerland) · 2023Article
- Deep Learning-Based Intelligent Forklift Cargo Accurate Transfer System.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Evaluations of different technologies and solutions for indoor localization exist but only a few are aimed at the industrial context. In this paper, we compare and analyze two prominent solutions based on Ultra Wide Band Radio (Pozyx) and Ultrasound (GoT), both installed in an industrial manufacturing laboratory. The comparison comprises a static and a dynamic case. The static case evaluates average localization errors over 90 s intervals for 100 ground-truth points at three different heights, corresponding to different relevant objects in an industrial environment: mobile robots, pallets, forklifts and worker helmets. The average error obtained across the laboratory is similar for both systems and is between 0.3 m and 0.6 m, with higher errors for low altitudes. The dynamic case is performed with a mobile robot travelling with an average speed of 0.5 m/s at a height of 0.3 m. In this case, low frequency error components are filtered out to focus the comparison on dynamic errors. Average dynamic errors are within 0.3-0.4 m for Pozyx and within 0.1-0.2 m for GoT. Results show an acceptable accuracy required for tracking people or objects and could serve as a guideline for the least achievable accuracy when applied for mobile robotics in conjunction with other elements of a robotic navigation stack.
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