AI Article Synopsis
- The paper focuses on combining data from multiple cameras to precisely determine the positions of fiducial markers, aiming to enhance precision and working area.
- It utilizes an adaptive Kalman algorithm for camera calibration and marker pose estimation, incorporating strategies to reduce measurement noise effects.
- The method is validated through Monte Carlo simulations across various scenarios, showing potential applications in physics experiments and other fields due to its adaptable nature.
Article Abstract
The paper addresses the problem of fusing the measurements from multiple cameras in order to estimate the position of fiducial markers. The objectives are to increase the precision and to extend the working area of the system. The proposed fusion method employs an adaptive Kalman algorithm which is used for calibrating the setup of cameras as well as for estimating the pose of the marker. Special measures are taken in order to mitigate the effect of the measurement noise. The proposed method is further tested in different scenarios using a Monte Carlo simulation, whose qualitative precision results are determined and compared. The solution is designed for specific positioning and alignment tasks in physics experiments, but also, has a degree of generality that makes it suitable for a wider range of applications.
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Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7248896 | PMC |
| http://dx.doi.org/10.3390/s20092746 | DOI Listing |
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