Robust Robotic Localization Using Visible Light Positioning and Inertial Fusion

Accurate indoor positioning is critical in the field of location-based services and robotics. Visible light positioning (VLP) technology is a promising technique as it can provide high accuracy positioning based on the existing lighting infrastructure. However, it is difficult to meet the requiremen...

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Vydáno v:IEEE sensors journal Ročník 22; číslo 6; s. 4882 - 4892
Hlavní autoři: Guan, Weipeng, Huang, Linyi, Hussain, Babar, Yue, C. Patrick
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 15.03.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Cameras
Computing time
Decoding
high accuracy
Inertial coordinates
Inertial fusion (reactor)
inertial measurement unit (IMU)
Inertial platforms
Light emitting diodes
Localization
Location awareness
Location based services
Outages
Robot kinematics
robotic localization
Robotics
Robots
robust indoor positioning
Robustness
sensors fusion
Three-dimensional displays
Visible light positioning (VLP)
ISSN:1530-437X, 1558-1748
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Popis
Shrnutí:Accurate indoor positioning is critical in the field of location-based services and robotics. Visible light positioning (VLP) technology is a promising technique as it can provide high accuracy positioning based on the existing lighting infrastructure. However, it is difficult to meet the requirement of multiple LED anchors in range for successful and accurate positioning. In this article, we proposed a loosely-coupled VLP-inertial fusion method for VLP, with an inertial measurement unit (IMU) and rolling shutter camera, to improve positioning robustness under LED shortage/outage. The efficacy of the proposed VLP scheme as well as the robustness under LED outage, handover situation and background light interference, are verified by real-world experiments. The results show that our proposed scheme can provide an average accuracy of 2.1 cm (stationary localization) and the average computational time in low-cost embedded platforms is around 33 ms.
Bibliografie:ObjectType-Article-1
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content type line 14
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2021.3053342