## 5 Conclusions  
Recently, drones have been used in various fields, and studies have been conducted using drones for inventory management. In some cases, a warehouse does not employ shelves and grids, such as a storage yard, so that the location of loading the inventory is not fixed. In this paper, we propose a 3D position estimation framework of the inventory based on the drone-mounted monocular camera and the QR code for inventory management automation, even in a storage yard without shelves or grids. We trained the segmentation model using our generated QR code dataset for the QR code area detection. We estimated the drone's 3D position through the ground QR code in which the positional information was stored. Furthermore, we derived the straight line passing through the 3D position of the inventory QR code attached to the inventory. Then, we selected the frame other than the outlier from the video frame, to estimate the inventory 3D position. In addition, we corrected the error in the estimated inventory 3D position by utilizing the continuity of the video frame. As a result of error correction, it was verified that the 3D position  
Figure 13: Visualization of the sine value between line pairs (**first row**) and the error of the estimated position (**second row**). A bright color means a value greater than a dark color.  
estimation performance of the inventory was improved. We confirmed that using the monocular camera mounted on the drone can estimate the inventory 3D position in a storage yard where the loading position of the inventory is not clearly distinguished. The proposed framework is also cost-effective by estimating the inventory 3D position using only a monocular camera. As a result of this study, we propose a framework that can estimate the 3D inventory position regardless of warehouse type. This means that inventory management automation can be applied to all types of warehouses. In addition, we have confirmed that it is possible to improve the 3D position estimation performance of the object through the angle between the epipolar lines and outlier detection of the drone's estimated 3D position.  
In future research, various types of QR code segmentation datasets will be additionally generated and applied to the model to improve the performance of the segmentation model. Furthermore, it is expected that the proposed framework and object detection model can be combined to estimate the 3D position of unmarked objects in the video frame. We evaluated the proposed framework with an ablation study. If we find other base models that can apply the proposed error correction methods in the future, we can evaluate the proposed methods by performing additional ablation studies in combination with them.  
Conceptualization, B.Y.; Methodology, B.Y.; Software, B.Y. and G.Y.; Formal Analysis, B.Y.; Data Curation, B.Y. and H.K.; Writing--Original Draft Preparation, B.Y.; Visualization, B.Y. and H.K.; Supervision, J.R. All authors have read and agreed to the published version of the manuscript.  
This research was financially supported by the Ministry of Trade, Industry and Energy (MOTIE) and Korea Institute for Advancement of Technology(KIAT) through the International Cooperative R&D program (Project No. P0016096).  
Not applicable.  
The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.  
The authors declare no conflict of interest.