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Light Acquisition

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Related Experiment Video

Updated: Jan 6, 2026

A Protocol for Real-time 3D Single Particle Tracking

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Practical passive visible light positioning with lightweight parameter updating.

Wei Sun, Ziwei Liu, Yihuai Xu

|November 4, 2025

Summary

This summary is machine-generated.

This study introduces a new method for visible light positioning (VLP) that adapts to different environments. The technique significantly reduces positioning errors in new scenes, improving accuracy for device-free applications.

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Last Updated: Jan 6, 2026

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Published on: January 3, 2018

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Published on: April 1, 2020

Area of Science:

Computer Vision
Indoor Positioning Systems

Background:

Passive visible light positioning (VLP) offers device-free usability but struggles with scene generalization.
Variations in environmental data features limit the adaptability of existing VLP methods.

Purpose of the Study:

To develop a lightweight parameter update scheme for enhancing VLP generalization across diverse scenes.
To improve the accuracy and adaptability of VLP systems in realistic, varying environments.

Main Methods:

A novel scheme combining knowledge distillation and contrastive learning for parameter updates.
Initial training of a convolutional neural network (CNN) VLP model.
Leveraging sparse data from new scenes and distilled knowledge to train a new network and extract encoder parameters for merging.

Main Results:

The proposed scheme successfully adapts the VLP model to new application scenes.
Maintained an average root mean square error (RMSE) of 22 cm across varying scenes.
Achieved a reduction in error to one-third compared to conventional methods.

Conclusions:

The lightweight parameter update scheme effectively enhances VLP generalization.
The method offers a significant improvement in positioning accuracy for device-free VLP systems in diverse environments.