[Paper Review] High Accuracy VLP based on Image Sensor using Error Calibration Method

Shihuan Chen, Weipeng Guan|arXiv (Cornell University)|Oct 1, 2020

Optical Wireless Communication Technologies24 references10 citations

TL;DR

This paper proposes a high-accuracy visible light positioning (VLP) system using a commercial image sensor by introducing two novel error calibration methods: Rotation Calibration and Dispersion Circle Calibration. By accurately estimating the rotation center and compensating for coordinate conversion errors, the method reduces average positioning error to 0.82 cm, achieving state-of-the-art performance in VLP using off-the-shelf image sensors.

ABSTRACT

In this paper, visible light positioning (VLP) where the receiver adopts a commercial image sensor is considered. We firstly analyze the theoretical limits and error source of the VLP system using image sensor. And then, we develop a VLP positioning model on the receiver movement and further propose two novel error calibration algorithms, namely Rotation Calibration Method and dispersion circle calibration method. The rotation algorithm estimates the rotation center in the image instead of treating the image center as the rotation center, leading to reduced positioning error. For the dispersion circle, it can offset the shift error created by the conversation between different coordinate during the positioning calculation. According to the experimental results, the average positioning error of the proposed methods can be reduced to 0.82cm, which achieve state-of-the-art in the VLP field.

Motivation & Objective

• To analyze theoretical limits and error sources in image sensor-based VLP systems.
• To address positioning errors caused by incorrect assumption of image center as rotation center in VLP systems.
• To correct shift errors introduced during coordinate conversion in VLP positioning calculations.
• To develop calibration methods that enhance accuracy without requiring specialized hardware.
• To achieve sub-centimeter positioning accuracy using commercial image sensors.

Proposed method

• Proposes a Rotation Calibration Method that estimates the true rotation center in the image instead of assuming the image center.
• Introduces a Dispersion Circle Calibration Method to correct shift errors from coordinate system conversions during positioning.
• Develops a VLP positioning model that accounts for receiver movement and geometric distortions.
• Uses experimental data to calibrate and validate the error compensation models in real-world conditions.
• Applies least-squares estimation to refine position estimates based on calibrated parameters.
• Combines both calibration methods in a unified framework to minimize cumulative error in VLP positioning.

Experimental results

Research questions

• RQ1 How does assuming the image center as the rotation center affect VLP accuracy?
• RQ2 What is the impact of coordinate system conversion on positioning error in image sensor-based VLP?
• RQ3 Can a calibration-based approach reduce positioning error without specialized hardware?
• RQ4 What level of accuracy can be achieved using only commercial image sensors with proper error calibration?
• RQ5 How do receiver motion and sensor geometry contribute to positioning error in VLP systems?

Key findings

• The proposed Rotation Calibration Method significantly reduces error by accurately estimating the true rotation center in the image plane.
• The Dispersion Circle Calibration Method effectively offsets shift errors caused by coordinate conversion during positioning.
• The combined use of both calibration methods reduces the average positioning error to 0.82 cm.
• The achieved error of 0.82 cm represents state-of-the-art performance in image sensor-based VLP systems.
• The method maintains high accuracy even under dynamic receiver movement, as validated through experimental evaluation.
• The approach is practical and deployable using only commercial image sensors, without requiring custom hardware.