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Robust Robotic Localization Using Visible Light Positioning and Inertial Fusion

Weipeng Guan, Linyi Huang, Babar Hussain, C. Patrick Yue

Year
2021
Citations
62

Abstract

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.

Keywords

Robustness (evolution)Inertial measurement unitComputer scienceArtificial intelligenceComputer visionInertial frame of referenceIndoor positioning systemPositioning systemReal-time computingInertial navigation system

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