UAVs typically rely on satellite navigation for positioning, yet this method proves ineffective in instances where the signal is inadequate or communication is disrupted. Visually based positioning technology has emerged as a reliable alternative. In this paper, we propose a novel end-to-end network, OBTPN. In the initial phase of the model, we optimized the distribution of attention within the primary network, aiming to achieve a balance between self-attention and cross-attention. Subsequently, we devised a feature fusion head, which enhanced the model’s capacity to process multi-scale information. OBTPN was successfully deployed on an NVIDIA Jetson TX2 onboard computer. This paper also proposes a high-altitude complex environment dataset, Crossview9, which addresses a research gap in the field of high-altitude visual navigation. The performance of the model on this dataset is also evaluated. Additionally, the dataset was processed to simulate a low-quality image environment to assess the model’s resilience in challenging weather conditions. The experimental results demonstrate that OBTPN_256 attains an accuracy of 84.55% on the RDS metric, thereby reaching the state-of-the-art (SOTA) level of the UL14 dataset. On the Crossview9 dataset, OBTPN_256 achieves an RDS score of 79.76%, also reaching the SOTA level. Most notably, the model’s high accuracy in low-quality image environments further substantiates its robustness in complex environments.

Unmanned aerial vehicle (UAV) self-localization in complex environments is critical when global navigation satellite systems (GNSSs) are unreliable. Existing datasets, often limited to low-altitude urban scenes, hinder generalization. This study introduces Multi-UAV, a novel dataset with 17.4 k high-resolution UAV–satellite image pairs from diverse terrains (urban, rural, mountainous, farmland, coastal) and altitudes across China, enhancing cross-view geolocalization research. We propose a lightweight value reduction pyramid transformer (VRPT) for efficient feature extraction and a residual feature pyramid network (RFPN) for multi-scale feature fusion. Using meter-level accuracy (MA@K) and relative distance score (RDS), VRPT achieves robust, high-precision localization across varied terrains, offering significant potential for resource-constrained UAV deployment.

Vision sensor-based simultaneous localization and mapping (SLAM) systems are essential for mobile robots to locate and generate spatial models of their surroundings. However, the majority of visual SLAM systems assume static settings, leading to significant accuracy degradation in dynamic scenes. We present SGDO-SLAM, a real-time RGB-D semantic-aware SLAM framework, building upon ORB-SLAM2 to address non-static environments. Firstly, a multi-constraint dynamic rejection method from coarse to fine is proposed. The method starts with coarse rejection by combining semantic and geometric information, followed by detailed rejection using depth information, where static quality weights are quantified based on depth consistency constraints. The method achieves accurate dynamic scene perceptions and improves the accuracy of the system’s positioning. Then, a position optimization method driven by static quality weights is proposed, which prioritizes high-quality static features to enhance pose estimation. Finally, a visualized dense point cloud map is established. We performed experimental evaluations on the TUM RGB-D dataset and the Bonn dataset. The experimental results demonstrate that SGDO-SLAM reduces the absolute trajectory error performance metrics by 95% compared to the ORB-SLAM2 algorithm, while maintaining real-time efficiency and achieving state-of-the-art accuracy in dynamic scenarios.

Satellite signal interruptions disable unmanned aerial vehicle (UAV) navigation, making visual localization a vital alternative. To improve robustness in oblique flight paths and complex environments, we propose the UAV visual geo-localization network with transformer and hypergraph feature aggregation enhancement (HTGL). First, we enhanced feature extraction capabilities by optimizing the downsampling strategy and attention allocation mechanism in the backbone network. Second, we designed the Hypergraph Feature Aggregation Enhancement (HFAE) module based on hypergraph-based adaptive correlation enhancement (HyperACE) to improve the model’s ability to capture higher-order correlations. Furthermore, we constructed the Complex Scene Rotation dataset (CSR10) and proposed a method for simulating winter scenes, thereby overcoming the limitations of existing research in terms of scenes, flight directions, and seasons. Additionally, two evaluation metrics—pixel distance root mean square error (PD-RMSE) and geographic distance root mean square error (GD-RMSE)—were introduced to enable a comprehensive assessment of algorithm performance. Experimental results show that HTGL achieved RDS scores of 85.95% (+1.4%), 83.64% (+4.1%), and 91.52% (+1.49%) on the UL14, UL14_ROT, and CSR10 datasets, respectively, demonstrating strong robustness in rotated and complex scenes. Actual deployment and flight tests on the Jetson Orin NX platform further validated the model’s excellent engineering practicality.

To enhance the performance of UAVs in detecting insulator self-explosion defects during power inspections, this paper proposes an insulator self-explosion defect recognition algorithm, SDA-YOLO, based on an improved YOLOv11s network. First, the SODL is added to YOLOv11 to fuse shallow features with deeper features, thereby improving the model’s focus on small-sized self-explosion defect features. The OBB is also employed to reduce interference from the complex background. Second, the DBB module is incorporated into the C3k2 module in the backbone to extract target features through a multi-branch parallel convolutional structure. Finally, the AIFI module replaces the C2PSA module, effectively directing and aggregating information between channels to improve detection accuracy and inference speed. The experimental results show that the average accuracy of SDA-YOLO reaches 96.0%, which is higher than the YOLOv11s baseline model of 6.6%. While maintaining high accuracy, the inference speed of SDA-YOLO can reach 93.6 frames/s, which achieves the purpose of the real-time detection of insulator faults.

Heterogeneous data from diverse sensor platforms can complement each other, providing more comprehensive and richer information for the analysis of complex scenarios. To effectively utilise this information for UAV geo-location tasks, accurate feature extraction and fusion methods are crucial. To address this challenge, we propose the Multi-Modal Geo Location Transformer Network (MMGLT), leveraging attention mechanisms to fuse cross-modal features. Unlike traditional dual modal fusion frameworks integrating only drone downward-view and satellite images, MMGLT innovatively uses a triple-input structure to process RGB images, LiDAR-derived auxiliary data, and satellite images. It also designs a tailored cross-attention mechanism to link visual semantics with remote sensing features f inely, enabling more robust multi-source feature learning. Subsequently, MMGLT employs a Global Attention Upsampling Head that refines features layer by layer via attention and adaptive up sampling; by combining dynamic feature re-allocation with multi scale pyramid fusion, it mitigates feature degradation and boosts representational power. With the aim of facilitating subsequent applications, we have successfully deployed the MMGLT model on the NVIDIA Jetson Orin NX compact computer. To evaluate the performance of MMGLT in the task, this paper constructs the CrossView-Van dataset. To the best of our knowledge, this is the first end-to-end geo-location dataset that integrates RGB images, high-precision remote sensing feature data, and satellite images. On this new dataset, MMGLT significantly outperforms existing single-modal methods, and achieves improvement in low-texture forest regions where traditional approaches underperform.