This paper presents detailed derivations and further explanations of the state transformation extended Kalman filter (ST-EKF) from the common frame error definition perspective. Both the system error and measurement models of strapdown inertial navigation system (SINS)/Odometer (OD) tightly-coupled navigation are derived based on the ST-EKF. Both theoretical analysis and test results indicate the effectiveness of the proposed velocity error definition on mitigating the covariance-inconsistency problem that is caused by the specific force calculation error in the EKF state transition matrix. The excellent covariance-consistency of the ST-EKF makes it not necessary to remove gyro and accelerometer bias errors from the initial alignment Kalman filter states. This phenomenon can ensure the subsequent integrated navigation performance. Single-position ground SINS alignment experiments by using a navigation grade inertial measurement unit (IMU) showed that the estimated yaw angle from the conventional 15-state EKF slowly diverged over time. Furthermore, this phenomenon became more distinct when the initial yaw angle error was larger. In contrast, the estimated yaw angle from the proposed 15-state ST-EKF was significantly more stable and less degraded by large initial yaw angle errors. Long- distance land-vehicle SINS/OD integrated navigation tests also exhibited the ST-EKF's higher positioning and heading accuracy than the EKF.

The ring laser gyro (RLG) dither axis will bend and exhibit errors due to the specific forces acting on the instrument, which are known as g-sensitive misalignments of the gyros. The g-sensitive misalignments of the RLG triad will cause severe attitude error in vibration or maneuver environments where large-amplitude specific forces and angular rates coexist. However, g-sensitive misalignments are usually ignored when calibrating the strapdown inertial navigation system (SINS). This paper proposes a novel method to calibrate the g-sensitive misalignments of an RLG triad in linear vibration environments. With the SINS is attached to a linear vibration bench through outer rubber dampers, rocking of the SINS can occur when the linear vibration is performed on the SINS. Therefore, linear vibration environments can be created to simulate the harsh environment during aircraft flight. By analyzing the mathematical model of g-sensitive misalignments, the relationship between attitude errors and specific forces as well as angular rates is established, whereby a calibration scheme with approximately optimal observations is designed. Vibration experiments are conducted to calibrate g-sensitive misalignments of the RLG triad. Vibration tests also show that SINS velocity error decreases significantly after g-sensitive misalignments compensation.

Most existing deep learning-based camera pose estimation methods rely solely on visible images, making them vulnerable to challenges such as drastic lighting variations and poor nighttime imaging quality. These limitations reduce the robustness and accuracy of traditional approaches. To address these issues, we propose VIFPE (Visible- and Infrared-Fusion-Based Pose Estimation), a novel framework that integrates features from both visible and infrared images to enhance pose estimation in urban environments. By leveraging the complementary advantages of visible images, which are rich in texture, and infrared images, which are resilient to illumination changes, VIFPE achieves robust and accurate localization across diverse lighting conditions. The framework consists of multiple key modules, including a feature extractor, a multimodal fusion module, a pose regressor, and a multi-task learning strategy. Specifically, VIFPE employs Multi-axis Vision Transformer (MaxViT) encoders to extract features from both modalities, which are then fused in a shared feature space through a cross-modal fusion module. A pose regression network is subsequently trained to estimate the camera’s position and orientation based on the fused representation. Our experimental results on diverse urban datasets demonstrate that VIFPE significantly outperforms conventional methods in terms of both accuracy and robustness, particularly under challenging lighting conditions. This work underscores the potential of multimodal fusion for camera localization in large-scale urban environments and lays a foundation for future advancements in the field.