## 4 Results and Discussions
### Comparison of 3D Position Estimation Results
Furthermore, the proposed framework is available for the low-cost drone-mounted monocular camera, unlike previous studies using expensive RFID sensors [16; 17]. [PERSON] et al. [35] proposed a method of estimating the 3D position of the drone using a QR code attached to the floor. The method proposed in this paper can estimate the drone's 3D position regardless of the attachment location of the QR code. In addition, the proposed method estimates the object 3D position using the estimated position of the drone. [PERSON] et al. [35] provided an experiment on an image that  
Figure 12: Results of the QR code decoding according to noise. synthesized the QR code without distortion. There is no object other than a QR code in the synthesized image. They proposed finding all contours in the image and predicting the contour with the largest range as the QR code. However, in a real environment, various contours occur due to various objects and noise, and the largest contours are rarely the QR code. In addition, several QR codes may be photographed in a single image, as in our experiment. Therefore, their proposed method cannot be used for the real-world data that we photographed. We performed the QR code decoding based the segmentation model. Therefore, the QR code decoding is possible without the need to set arbitrary contours. As a result, our proposed framework works on an image from the real world, where multiple contours occur. We further estimate the object 3D position through the 3D position estimation of the drone. For this, we proposed two new error correction for improving of the object 3D position performance. In our previous work [1], we conducted the experiment on the drone's 3D position estimation of the proposed method in a real-world environment. The QR code that was taken has a shape distortion similar to the working environment. Nevertheless, our proposed method estimated the drone's 3D position with an error of about 4.7%. We estimated the drone's 3D position from the four markers and estimated the object 3D position using the estimated two-point drone 3D position [1]. However, it is difficult for four markers to be photographed on one image in a real environment. Furthermore, it is difficult to accurately shoot the QR code at the desired position due to various noises. To solve this problem, this paper proposed the object 3D position estimation using continuously filmed video frames. Unlike previous work, the proposed method is designed to be practically usable by estimating the object 3D position through one marker. To drastically reduce the number of markers for object 3D position estimation, we proposed a method utilizing the alignment pattern of the QR code. We also proposed novel two error correction methods for improving the performance of the object 3D position estimation. We eliminated the outlier frame of estimated drone 3D position utilizing continuity to improve the performance of object 3D position estimation. Furthermore, we proposed an epipolar line-based error correction method for the object 3D position estimation from drone 3D positions estimated in the video frame. As a result of the experiment on the video taken in an outdoor environment, it was confirmed that it was possible to estimate the object 3D position using one marker. In addition, it was confirmed that the performance of object 3D position estimation was improved by the proposed error correction methods. A new correction method is proposed through the relationship between the video frames for the error of the estimated inventory 3D position to improve the accuracy of inventory 3D position estimation. The proposed correction method can itself be used as a method of correcting the position of the object photographed in the video. In summary, this paper proposes a novel framework that improves the estimated 3D position accuracy of the inventory based on deep learning and DBSCAN to the error correction method available in the video frame. However, the following additional attempts may be made to improve the performance.