Infrared–visible image fusion (IVIF) integrates complementary thermal and photometric cues for surveillance, remote sensing, and autonomous perception. Existing surveys, while comprehensive, provide limited guidance for design-to-deployment and seldom relate fusion quality to task outcomes or device constraints. This work provides a unified perspective that organizes IVIF methods along an interface-attention-alignment coordinate system covering classical spatial/transform pipelines and contemporary deep paradigms (generative, discriminative, multi-task, hybrid/Transformer, dynamic). Building on literature through 2025, we synthesize fidelity-robustness-efficiency trade-offs and introduce a comparison-to-deployment protocol that couples fusion metrics with task accuracy (AP/mIoU), latency, memory footprint, and condition-performance characterization (misregistration, noise, illumination/weather). We consolidate Transformer/hybrid coverage with practical recipes and focused guidance on temporal consistency, robustness auditing, and physics-grounded interpretability. Compared with previous reviews, our survey concurrently addresses four under-covered dimensions-video temporal consistency, robustness auditing, task-aware evaluation, and deployment reporting-and distills a practical checklist linking architectural choices to operating conditions and hardware budgets, enabling reproducible, task-relevant IVIF practice.

An assessment of the error inherent in rotation vector algorithm utilized in strapdown inertial navigation system is presented, which shows that the accuracy cannot be improved boundlessly only by increasing the number of the used samples. After reaching the upper limit for accuracy, increasing sample size would only enlarge the algorithm error rather than improve the performance of rotation vector algorithm. After accuracy assessment, a new method to construct strapdown attitude algorithm is originated. Unlike previous algorithms adopting rotation vector, the new method utilizes the direction cosine matrix differential equation as its basis, and discretizes this differential equation with Taylor time series meanwhile extracts angular velocity and its derivatives by polynomial fitting. With this method, three direction cosine matrix attitude algorithms, taking 4, 5, 6 samples respectively, are deduced, and their performances are evaluated with coning motion and severe maneuver profile. The results show that the new algorithms based on direction cosine matrix differential equation can effectively handle the coning error and severe maneuver error.

To address the accuracy requirements of initial alignment of high-precision inertial navigation systems (INSs), gravity disturbance compensation for INSs based on a spherical harmonic model is investigated herein. First, the horizontal component of gravity disturbance at an alignment point is calculated using the high-resolution Earth Gravity Model EIGEN-6C4 and then compensated to the initial alignment. Subsequently, the self-alignment algorithm of solidified coordinate frame is used to derive the misalignment angle equation of gravity disturbance affecting the initial alignment. Meanwhile, the coupling relationship between the measurement error of an inertial unit and the gravity disturbance is simulated and analyzed. Finally, a laser strapdown inertial navigation system experiment is performed. The simulation result shows that the pitch angle, roll angle, and heading angle errors reduced by $27.41^{\prime \prime }$ , $- 0.37^{\prime \prime }$ , and $6.72^{\prime \prime }$ , respectively, after the gravity disturbance compensation. Experiment result shows that the alignment performance after compensation has been improved and the heading angle error is reduced by $6.76^{\prime \prime }$ . The simulations and experiments results validate the theoretical analysis.

To address the urgent demand for autonomous rapid initial alignment of vehicular inertial navigation systems in complex battlefield environments, this study overcomes the technical limitations of traditional stationary base alignment methods by proposing a robust moving-base autonomous alignment approach based on multi-source information fusion. First, a federal Kalman filter-based multi-sensor fusion architecture is established to effectively integrate odometer, laser Doppler velocimeter, and SINS data, resolving the challenge of autonomous navigation parameter calculation under GNSS-denied conditions. Second, a dual-mode fault diagnosis and isolation mechanism is developed to enable rapid identification of sensor failures and system reconfiguration. Finally, an environmentally adaptive dynamic alignment strategy is proposed, which intelligently selects optimal alignment modes by real-time evaluation of motion characteristics and environmental disturbances, significantly enhancing system adaptability in complex operational scenarios. The experimental results show that the method proposed in this paper can effectively improve the accuracy of vehicle-mounted alignment in motion, achieve accurate identification, effective isolation, and reconstruction of random incidental faults, and improve the adaptability and robustness of the system. This research provides an innovative solution for the rapid deployment of special-purpose vehicles in GNSS-denied environments, while its fault-tolerant mechanisms and adaptive strategies offer critical insights for engineering applications of next-generation intelligent navigation systems.

In autonomous driving, detecting small and occluded objects remains a substantial challenge due to the complexity of real-world environments. To address this, we propose RSO-YOLO, an enhanced model based on YOLOv12. First, the bidirectional feature pyramid network (BiFPN) and space-to-depth convolution (SPD-Conv) replace the original neck network. This design efficiently integrates multi-scale features while preserving fine-grained information during downsampling, thereby improving both computational efficiency and detection performance. Additionally, a detection head for the shallower feature layer P2 is incorporated, further boosting the model’s capability to detect small objects. Second, we propose the feature enhancement and compensation module (FECM), which strengthens features in visible regions and compensates for missing semantic information in occluded areas. This module improves detection accuracy and robustness under occlusion. Finally, we propose a lightweight global cross-dimensional coordinate detection head (GCCHead), built upon the global cross-dimensional coordinate module (GCCM). By grouping and synergistically enhancing features, this module addresses the challenge of balancing computational efficiency with detection performance. Experimental results demonstrate that on the SODA10M, BDD100K, and FLIR ADAS datasets, RSO-YOLO achieves mAP@0.5 improvements of 8.0%, 10.7%, and 7.2%, respectively, compared to YOLOv12. Meanwhile, the number of parameters is reduced by 15.4%, and model complexity decreases by 20%. In summary, RSO-YOLO attains higher detection accuracy while reducing parameters and computational complexity, highlighting its strong potential for practical autonomous driving applications.

The random error of fiber optic gyros is a critical factor affecting their measurement accuracy. However, the statistical characteristics of these errors exhibit time-varying properties, which degrade model fidelity and consequently impair the performance of random error suppression algorithms. To address these issues, this study first proposes a recursive dynamic Allan variance calculation method that effectively mitigates the poor real-time performance and spectral leakage inherent in conventional dynamic Allan variance techniques. Subsequently, the recursive dynamic Allan variance is integrated with the process variance estimation of Kalman filtering to construct a dual-adaptive Kalman filter capable of autonomously switching and adjusting between model parameters and noise variance. Finally, both static and dynamic validation experiments were conducted to evaluate the proposed method. The experimental results demonstrate that, compared to existing algorithms, the proposed approach significantly enhances the suppression of angular random walk errors in fiber optic gyros.