# 3D Positioning of Drones through Images
## 5 Conclusions  
UAVs are now widely used in military, civilian, and commercial fields, with autonomous positioning capability being core to their mission completion. Currently, UAVs mainly rely on global satellite positioning systems for geolocation. However, in actual flight operations, factors such as obstruction, electromagnetic interference, and adverse weather can affect the strength of satellite positioning signals received by UAVs. In extreme cases, this can lead to UAVs operating in GNSS-denied environments, where they lose autonomous positioning capability and cannot conduct flight operations.  
Therefore, solving the positioning problem for UAVs in GNSS-denied environments has become a research hotspot. This paper uses artificial intelligence technology and deep learning visual algorithms to enable UAVs to obtain their current geographical location by matching ground images captured by their onboard cameras in GNSS-denied environments. The main research content and work summary of this paper are as follows:  
(1) We proposed a multi-altitude, multi-style UAV image dataset, UL14 plus, based on which we conducted training and testing for UAV image positioning and altitude determination.  
(2) We proposed a vision-based positioning algorithm, MH-FPI, based on an attention mechanism. The network model design references related work in the field of object tracking. By inputting two UAV images with the same center point and a satellite image into the model, it uses the Twins-PCPVT-S backbone network for feature extraction. The  
Figure 15: Relative error in calculating height at different altitudes.  
satellite and UAV images undergo two rounds of matching, producing coarse and precise positioning. Subsequently, it outputs a feature heatmap, predicting the UAV's position on the satellite image map based on the heat value distribution on the feature heatmap, thus obtaining the corresponding geographical coordinates. Ultimately, the MH-FPI network model achieved a positioning accuracy of nearly 81% within a 20 m range and an RDS score of 76.3 on the UL14 plus dataset test set.  
(3) We proposed a method for calculating UAV flight altitude through image analysis. By using the model to calculate four-point coordinates in a single UAV image, we derive the actual area of the UAV's captured image and calculate the flight altitude based on the proportional relationship. Additionally, we introduced a sequential quadratic programming method to optimize the area calculation. The final relative error in altitude determination is 18.16%.  
The positioning and altitude determination methods proposed in this paper have achieved good accuracy for multi-altitude scenarios. The method of directly estimating altitude from images is newly proposed, with its advantage being that each frame of UAV image can independently calculate altitude without relying on data support from other sensors. Currently, the accuracy achieved is not high, mainly due to positioning offsets leading to deviations in altitude calculations. The source of error is relatively singular, leaving room for future improvements. Ideally, further research is needed on the problem of enhancing altitude determination accuracy as a supplement and aid to UAV altitude sensors in GNSS-denied environments. Methods such as image optical flow and joint use of multiple frames of images could be considered.  
Conceptualization, E.Z.; Software, J.F.; Validation, Y.Y.; Writing--original draft, J.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors declare no conflicts of interest.