Temporal-redundant triangulation algorithm in small scale camera network systems

This work addresses the problem of 3D target localization using a camera network affected by calibration errors. We formalize the reconstruction problem and present the state-of-the-art reconstruction procedures via triangulation. To improve performance in scenarios with a limited number of cameras, we propose a temporal redundant triangulation strategy that exploits multiple and consecutive observations from each camera in the network. By aggregating these measurements, our method enhances robustness against calibration errors without increasing hardware complexity. To validate this, we propose a Monte Carlo simulation campaign in which we compare our proposed approach with the classical triangulation methods.