Passive Multi-Target Visible Light Positioning Based on Multi-Camera Joint Optimization
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866916892127526912 |
|---|---|
| author | Pan, Wenxuan Yang, Yang Wei, Dong Zhang, Meng Zhu, Zhiyu |
| author_facet | Pan, Wenxuan Yang, Yang Wei, Dong Zhang, Meng Zhu, Zhiyu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_02418 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Passive Multi-Target Visible Light Positioning Based on Multi-Camera Joint Optimization Pan, Wenxuan Yang, Yang Wei, Dong Zhang, Meng Zhu, Zhiyu Signal Processing 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. |
| title | Passive Multi-Target Visible Light Positioning Based on Multi-Camera Joint Optimization |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2506.02418 |