## 5 Conclusions  
This paper presents a novel methodology for enhancing pose estimation accuracy and precision using multiple graphical markers. The developed algorithm improves measurement performance by averaging pose estimates from multiple markers while incorporating two-stage fault detection mechanisms: removal of invalid markers from the marker array and elimination of outlier measurements showing abrupt temporal variations.  
The performance of the developed algorithm was validated through experiments that used both fixed and drone-mounted cameras and compared single-marker and multiple-marker configurations. In the experiments using a fixed camera, the multiple-marker approach demonstrated superior performance, with reduced mean errors and standard deviations in both position and orientation measurements, indicating improvements in both accuracy and precision. The drone-mounted camera experiments further confirmed these findings, with the multiple-marker method showing significantly lower standard deviations in pose measurements compared with the single-marker approach, demonstrating enhanced measurement precision in actual drone applications.  
While our research has successfully demonstrated improvements in measurement accuracy and precision through the use of multiple markers, several aspects warrant further investigation to enhance the system's overall performance and applicability. First, regarding the current implementation's robustness under diverse environmental conditions, we used fixed threshold values for invalid marker detection and outlier detection, along with a simple averaging method to aggregate information from multiple markers. Further research is needed to develop an adaptive thresholding mechanism that adjusts according to environmental changes, and to explore the application of various filters, such as weighted moving averages or Kalman filters, instead of simple averaging. Additionally, considering the potential benefits of broader field-of-view imaging systems, future studies should investigate pose estimation using 360-degree cameras, which can detect markers  
Figure 8: Estimation of representative marker pose with drone-mounted camera. Red and blue represent experimental results using single marker and multiple marker. (**a**) Position along x-axis, (**b**) Position along y-axis, (**c**) Position along z-axis, (**d**) Roll, (**e**) Pitch, (**f**) Yaw.  
over a wider range compared with monocular cameras. This research direction would require developing specialized image-preprocessing techniques and calibration methods to effectively utilize the comprehensive visual information provided by 360-degree cameras.  
The developed algorithm contributes to the advancement of robust pose estimation methodologies in mobile robotics and autonomous systems, with potential applications across various domains where precise position and orientation measurements are crucial.  
Conceptualization, J.H.B. and S.J.; methodology, D.K., J.H.B. and S.J.; software, D.K.; validation, D.K.; formal analysis, J.H.B. and S.J.; writing--original draft preparation, D.K.; writing--review and editing, J.H.B. and S.J.; visualization, J.H.B.; supervision, J.H.B. and S.J.; funding acquisition, S.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by a 2022 research grant from Sangmyung University.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.