Unmanned Aerial Vehicle (UAV) swarms play a pivotal role in the low-altitude economy. Accurate swarm positioning information underpins mission coordination, resource optimization, and efficient scheduling among drones, thereby facilitating the sustainable advancement of the low-altitude economy. In complex environments, however, Global Satellite Navigation System (GNSS) signals may be disrupted, rendering it difficult for UAV swarms to obtain accurate positioning data and compromising their ability to function collaboratively. To address this challenge in GNSS-denied environments, this paper presents a UAV swarm positioning method that integrates monocular vision and ranging information. Visual Odometry (VO) is employed to enable autonomous positioning for each UAV within the swarm. A communication framework is designed to transmit only essential data, including visual keyframes, pose frames, and map points, to the central server, thus reducing communication bandwidth. The concept of pose frame is introduced to address the limitation that keyframes cannot fuse with ranging information. The central server aligns maps from different UAVs based on the common view relationships between keyframes or the constraints between pose frames and their corresponding ranging data. The server then fuses and optimizes these maps using both visual and ranging information, achieving accurate swarm positioning. After global optimization, the server sends the corrected keyframe and map point data back to the local map of UAV's VO to further enhance positioning accuracy. The proposed method is validated through simulations and experiments. Results demonstrate that the swarm positioning error is reduced to 0.49 m, outperforming current state-of-the-art visual positioning methods. Additionally, the scale error is reduced to 3.2%, effectively resolving the problem of scale ambiguity inherent in monocular visual positioning. This method proposed enables precise UAV swarm positioning based solely on inter-UAV ranging information, eliminating the need for shared visual features, and providing robust positioning data for UAV swarms operating in complex environments.

Formation flying has emerged as a prominent aircraft operational mode, necessitating precise relative positioning of aircraft within the formation as a fundamental requirement. In Global Navigation Satellite System (GNSS) -denied environments, aircraft experience a loss of global positioning information, making it challenging to derive relative aircraft positioning solely from local data sources. To address the relative positioning challenge among aircraft within a formation in GNSS-denied environments, this paper introduces a method that integrates data link and inertial navigation. Firstly, the inertial navigation method is employed to give a real-time calculation of the positioning information of each aircraft, which is then transmitted to other aircraft through its respective data link. Subsequently, each aircraft conducts measurements of the relative positions between the aircraft in the formation by using the received information from other aircraft and the data link. Finally, leveraging the continuous time series of inertial navigation data and data link measurements, a relative pose optimization factor graph is constructed to solve the relative poses between the aircraft in real time. Simulation and experimental verification are undertaken, employing a two-aircraft formation as a case study. The outcomes indicate that this method proposed enables real-time estimation of the relative positions of aircraft within the formation. Experimental results demonstrate that this method reduces the distance error measured by the data link by 76%, and can provide accurate and reliable relative position information for formation flight.