Global navigation satellite systems (GNSS) can provide high-quality location information in outdoor environments. In indoor environments, GNSS cannot achieve accurate and stable location information due to the obstruction and attenuation of buildings together with the influence of multipath effects. Due to the rapid development of micro-electro-mechanical system (MEMS) sensors, today’s smartphones are equipped with various low-cost and small-volume MEMS sensors. Therefore, it is of great significance to study indoor pedestrian positioning technology based on smartphones. In order to provide pedestrians with high-precision and reliable location information in indoor environments, we propose a pedestrian dead reckoning (PDR) method based on Transformer+TCN (temporal convolutional network). Firstly, we use IMU (inertial measurement unit)/PDR pre-integration to suppress the inertial navigation divergence. Secondly, we propose a step length estimation algorithm based on Transformer+TCN. The Transformer and TCN networks are superimposed to improve the ability to capture complex dependencies and improve the generalization and reliability of step length estimation. Finally, we propose factor graph optimization (FGO) models based on sliding windows (SW-FGO) to provide accurate posture, which use accelerometer (ACC)/gyroscope/magnetometer (MAG) data to establish factors. We designed a fusion positioning estimation test and a comparison test on step length estimation algorithm. The results show that the fusion method based on SW-FGO proposed by us improves the positioning accuracy by 29.68% compared with the traditional FGO algorithm, and the absolute position error of the step length estimation algorithm based on Transformer+TCN in pocket mode is mitigated by 42.15% compared with the LSTM algorithm. The step length estimation model error of Transformer+TCN is 1.61%, and the step length estimation accuracy is improved by 24.41%.

Visual scene matching navigation (VSMN) for unmanned aerial vehicles (UAVs) boasts advantages such as high precision, high reliability, and autonomy. The biggest challenge lies in the tension between local fine-grained information and global semantics, as well as limited generalization ability in real-world environments. While existing Transformer-based cross-view geolocation methods enhance global context modeling capabilities, they still generally face issues such as high demands on training data and computational resources, insufficient fusion of local fine-grained information and global semantics, and real-time performance in real-world complex environment. To address these problems, we propose a scene matching and localization algorithm based on the Siamese-ViT. For feature extraction, we use the ViT model to extract global features and K-means clustering to aggregate local features. Combined with the global features extracted by the ViT, a robust local–global feature representation vector is generated. For feature matching, incremental principal component analysis (IPCA) is used to reduce the dimensionality of the high-dimensional feature space, and a KD-tree is constructed for fast feature retrieval to improve matching efficiency. We validated our algorithm on the University-1652 dataset and a dataset of real-world satellite-drone image pairs. The results show that our Siamese-ViT outperforms other models in both Recall and AP. We conduct flight experiments in real-world environments, capturing drone images of complex scenes, including farmland, urban buildings, and waterways. The results show that, at a flight altitude of 350 m, our algorithm achieves an average absolute value of 6.2063 m for latitude, 6.7552 m for longitude, and 10.1922 m for horizontal error. Therefore, our Siamese-ViT demonstrates ideal overall positioning accuracy.