Intelligent matching of heterogeneous remote sensing images is a common basic problem in the field of intelligent remote sensing image processing. Aiming at the difficulty of matching satellite-aerial remote sensing images, this article proposes an intelligent matching method for heterogeneous remote sensing images based on style transfer. First, based on the idea of image style transfer of a generative adversarial networks, this method improves the conversion effect of the model on heterogeneous images by constructing a new generative network loss function and converts satellite images into aerial images. Then, the advanced deep learning-based matching algorithms D2-Net and LoFTR are used to achieve matching between the generated aerial image and the original aerial image. Finally, this transformation relationship is mapped to the corresponding satellite–aerial image pair to obtain the final matching result. The image style transfer experiments and the matching experiments we carry out under different test datasets show that the smooth cycle-consistent generative adversarial networks proposed in this article can effectively reduce the complexity of the algorithm and improve the quality of image generation. In addition, combining it with deep learning-based feature-matching methods can effectively improve the accuracy and robustness of the matching algorithm. Our code and data can be found at: https://gitee.com/AZQZ/intelligent-matching .

UAVs are increasingly prominent in the competition for space due to their multiple characteristics, such as strong maneuverability, long flight distance, and high survivability. A new online safe flight control method based on constrained reinforcement learning is proposed for the intelligent safety control of UAVs. This method adopts constrained policy optimization as the main reinforcement learning framework and develops a constrained policy optimization algorithm with extra safety budget, which introduces Lyapunov stability requirements and limits rudder deflection loss to ensure flight safety and improves the robustness of the controller. By efficiently interacting with the constructed simulation environment, a control law model for UAVs is trained. Subsequently, a condition-triggered meta-learning online learning method is used to adjust the control raw online ensuring successful attitude angle tracking. Simulation experimental results show that using online control laws to perform aircraft attitude angle control tasks has an overall score of 100 points. After introducing online learning, the adaptability of attitude control to comprehensive errors such as aerodynamic parameters and wind improved by 21% compared to offline learning. The control law can be learned online to adjust the control policy of UAVs, ensuring their safety and stability during flight.

Accurate LiDAR-inertial SLAM in dynamic environments remains challenging due to transient moving objects and long-horizon drift accumulation. Point-only formulations offer weak structural constraints, while post-hoc dynamic filtering cannot promptly inform the frontend pose estimation and data association. To address these issues, we present SPLIN, a structured plane-based LiDAR-inertial SLAM framework that couples real-time dynamic suppression with uncertainty-consistent estimation. First, we introduce Incremental Static-referenced Dynamic Object Removal (ISDOR), a lightweight module that detects and suppresses moving objects upon their first appearance at the frame level. Second, we develop a real-time, tightly coupled Point-Plane LiDAR-Inertial Odometry (PPLIO) based on an iterated Kalman filter. PPLIO integrates ISDOR, an efficient plane extraction module, and an adaptive downsampling strategy, enabling robust operation across scenes with widely varying scales and dynamics. To mitigate long-term odometric drift, we further employ uncertainty-aware factor-graph optimization in the backend, yielding a complete, accurate, and efficient LiDAR-inertial SLAM system. Extensive experiments demonstrate the effectiveness of SPLIN: ISDOR yields, on average, a 13.47% F1 improvement (best F1 on 15/17 sequences), a 26.67% precision gain, and a 64.26% reduction in runtime over state-of-the-art baselines. Building on ISDOR, SPLIN achieves the lowest localization error on 28/34 sequences—including highly dynamic and low-feature environments—while maintaining real-time inference at 6.94-36 ms per frame. Ablation studies further validate the contributions of ISDOR and the loop closure strategy to the overall system accuracy.