# 3D Positioning of Drones through Images
## 1 Introduction  
Unmanned Aerial Vehicles (UAVs) have broad application prospects in numerous fields, including surveying and mapping [1], agriculture [2], rescue operations [3], and military applications. However, during actual flight operations, UAVs often encounter \"denial environments\" where GPS signals are lost or interfered with, resulting in the loss of precise positioning and altitude determination capabilities. This not only greatly reduces the operational efficiency and reliability of UAVs but may also lead to serious consequences such as loss of control. Therefore, achieving autonomous positioning and assisted altitude determination for UAVs in denial environments has long been a major challenge in this field. Cross-View Geo-Localization based on computer vision technology is one effective approach to address this issue. Initially, the University-1652 dataset [4] introduced the use of drone views for cross-view geo-localization. University-1652 proposed tasks such as drone view target localization and drone navigation, expanding the application scope of drone geo-localization. The basic idea is to estimate the precise location of the UAV by matching the drone's overhead imagery with satellite orthoimages of known locations. Early image retrieval localization methods used metric learning to continuously bring the feature vectors of similar areas in drone and satellite images closer together while pushing apart the feature vectors of different areas, thus achieving image matching. This method requires preparing a vast amount of satellite imagery in advance, segmenting each image, extracting features, encoding features, and storing them, resulting in enormous computational and storage costs. Moreover, in practical applications, drone images often cannot be perfectly aligned with the images in the database, leading to localization errors. To address these issues, the authors proposed the FPI (Finding Point with Image) [5], a novel end-to-end localization paradigm. FPI borrows ideas from the single object tracking domain, using both drone and satellite images as input. It extracts features from both images through a network, calculates the similarity between their features, and generates a heatmap. The drone's position corresponds to the peak point in the heatmap. By utilizinga single backbone to simultaneously process drone and satellite images, it integrates traditional feature extraction and relationship modeling. During the feature extraction process, it effectively utilizes the powerful performance of the Transformer mechanism, enabling information interaction between drone and satellite features. The characteristics of this method are: (1) In the early stages of feature extraction, the model can determine which relevant features to retain, reducing the loss of target information. (2) The Transformer mechanism establishes interaction channels between drone and satellite images, thereby improving performance. (3) Feature maps of different sizes are generated and preserved at each stage, making subsequent interaction between drone and satellite features possible. While the above methods have achieved good results, several problems still exist: (1) The network training process is based on drone images captured at 80-100 m altitude. At this height, accuracy is relatively high, but when the altitude changes, the features of same-sized drone images change, and the accuracy of models trained on 80-100 m altitude images decreases significantly, indicating poor model generalization. (2) During UAV flight, satellite signals assist in determining flight altitude. In denial environments, the z-direction GPS signal is also lost, necessitating a method to assist in determining the flight altitude. How do UAVs determine their altitude during flight? The current mainstream approach is multi-sensor data fusion, including accelerometers and gyroscopes, barometers, GNSS, and ultrasonic rangefinders.