Passive Multi-Target Visible Light Positioning Based on Multi-Camera Joint Optimization

TL;DR

This paper introduces a passive multi-camera visible light positioning method that uses joint optimization to achieve millimeter-level accuracy without dedicated infrastructure.

Contribution

It proposes a novel multi-camera joint optimization algorithm for passive VLP, improving accuracy and scalability over existing methods.

Findings

Achieves millimeter-level positioning accuracy.

Improves over state-of-the-art by 19%.

Average error as low as 5.63 mm.

Abstract

Camera-based visible light positioning (VLP) has emerged as a promising indoor positioning technique. However, the need for dedicated luminaire infrastructure and on-target cameras in existing algorithms may limit their scalability and increase deployment costs. To address these limitations, this letter proposes a passive VLP algorithm based on Multi-Camera Joint Optimization (MCJO). In the considered system, multiple ceiling-mounted pre-calibrated cameras continuously capture images of targets with unmodulated point light sources, and can simultaneously localize these targets at the server. In particular, MCJO comprises two stages: It first estimates target positions via linear least squares (LLS) from multi-view projection rays; then refines these positions through nonlinear joint optimization to minimize the reprojection error. Simulation results show that MCJO can achieve millimeter-level accuracy, with an improvement of 19% over an LLS-based state-of-the-art algorithm. Experimental results further show that MCJO achieves an average position error as low as 5.63 mm.