Depth estimation using monocular sensors is an important and basic task in computer vision. It has a wide range of applications in robot navigation, autonomous driving, etc., and has received extensive attention from researchers in recent years. For a long time before, monocular depth estimation was based on convolutional neural networks, but its inherent convolution operation showed limitations in modeling large-scale dependence. Using Transformers instead of convolutional neural networks to perform monocular depth estimation provides a good idea, but there is a problem that the calculation complexity is too high and the number of parameters is too large. In response to these problems, we proposed Swin-Depth, which is a Transformer-based monocular depth estimation method that uses hierarchical representation learning with linear complexity for images. In addition, there is an attention module based on multi-scale fusion in Swin-Depth to strengthen the network’s ability to capture global information. Our proposed method effectively reduces the excessive parameters in the monocular depth estimation using transformer, and a large number of research experiments show that Swin-Depth has achieved state-of-the-art in challenging datasets of indoor and outdoor scenes.

Due to the functional limitations of a single UAV, UAV clusters have become an important part of smart cities, and the relative positioning between UAVs is the core difficulty in UAV cluster applications. Existing UAVs can be equipped with satellite navigation, radio navigation, and other positioning equipment, but in complex environments, such as urban canyons, various navigation sources cannot achieve full positioning information due to occlusion, interference, and other factors, and existing positioning fusion methods cannot meet the requirements of these environments. Therefore, demand exists for the real-time positioning of UAV clusters. Aiming to solve the above problems, this paper proposes multisource fusion UAV cluster cooperative positioning using information geometry (UCP-IG), which converts various types of navigation source information into information geometric probability models and reduces the impact of accidental errors, and proposes the Kullback–Leibler divergence minimization (KLM) fusion method to achieve rapid fusion on geometric manifolds and creatively solve the problem of difficult fusion caused by different positioning information formats and parameters. The method proposed in this paper is compared with the main synergistic methods, such as LS and neural networks, in an ideal scenario, a mutation error scenario, and a random motion scenario. The simulation results show that by using UAV cluster movement, the method proposed in this paper can effectively suppress mutation errors and achieve fast positioning.

Unmanned aerial vehicles (UAVs) have become the core infrastructure of smart cities because of their fast, flexible, and strong environmental adaptability. However, signal occlusion caused by the urban canyon effect will seriously affect its global navigation satellite system (GNSS) reliability. The fusion algorithm represented by Kalman Filter cannot meet the real-time and stability requirements of UAV high maneuvering flight positioning due to its high complexity. In this article, integrated GNSS, inertial navigation, and wireless base station navigation, a 3-D UAV positioning method called GIW-UP, based on information geometry, is proposed. It converts the information of various types of navigation sources into probability density functions, and then the fusion is realized from the perspective of information probability. Given the differences in the information output time and navigation parameters of various navigation sources, the proposed GIW-UP method is compared with the least squares (LS) method, the unscented Kalman filter (UKF) method, and the neural network-based multisensor two-stage fusion (MTFA) method in three aspects: stability, convergence speed, and computational complexity. The results show that the GIW-UP can effectively reduce the fusion computational complexity and improve positioning stability.

With the development of vehicle applications such as intelligent transportation and autonomous driving, the application fields based on location services have increasingly higher requirements for vehicle positioning reliability and real-time accuracy. However, the existing single navigation source of vehicles makes it difficult to realize real-time and high-precision positioning in different scenarios. The current multi-source information fusion methods have the problems of low generalization ability, poor expansibility, and high computational complexity, so it is challenging to apply in the field of vehicle positioning. To solve the above problems, this paper proposes a vehicle heterogeneous multi-source information fusion positioning method (MIFP) based on information probability, which converts the multiple heterogeneous navigation sources into information probability models to realize the unification of the timefrequency parameter format and designs an information fusion algorithm to realize the rapid fusion based on the theory of relative entropy. Through simulation tests and experimental verification by comparing with mainstream information fusion methods, such as the UKF method, the FGA method, and the NNA method, the MIFP method has high positioning accuracy and strong real-time performance. It can effectively solve the problems of weak expansion ability and large calculation amounts of current vehicle fusion positioning models. In the case of interference or mutation, the MIFP method can also suppress the influence of sudden errors on vehicle positioning.