Aiming at the problem of weak initial alignment anti-interference ability of vehicle mounted single axis rotating inertial navigation system under complex conditions, which leads to poor alignment accuracy and navigation accuracy. In this paper, we combine the reverse navigation with the improved Sega-Husa filtering algorithm, and propose an initial alignment method based on reverse navigation Sega-Husa kalman filtering (RSKF). Firstly, coarse alignment is performed and key data are stored at the same time. After coarse alignment, the initial posture of coarse alignment and stored data are used for adaptive filtering alignment based on forward and reverse navigation. This method can fully mine and use the data. The experimental results show that compared to traditional algorithms, the method significantly augments the accuracy of alignment, and thereby elevating the navigation precision of the vehicle’s single-axis rotational inertial guidance system.

In a long-endurance underwater vehicle rotational inertial navigation system (RINS), the rotation strategy significantly impacts navigation accuracy. To address the limitations of the dual-axis 16-position rotation modulation scheme, specifically, its large rotation angles and inability to modulate errors arising from the coupling of scale factors and Earth’s rotation rate. This article proposes a geocentric frame-based tri-axial 32-position rotation modulation method. This method suppresses navigation errors by altering the direction of the rotation axis and increasing the rotation sequence. It simultaneously isolates the Earth’s rotation rate, thereby eliminating the coupling error between inertial sensor scale factors and Earth rotation, and enhancing system navigation accuracy. Experimental results demonstrate that the system achieves a navigation positioning accuracy of approximately 2.2 nautical miles (n mile) over 72h. Compared to a single-axis RINS, positioning accuracy is improved by a factor of 8. Compared to a dual-axis RINS, positioning accuracy is enhanced by 0.75 n mile. These results validate the effectiveness of the proposed method in improving navigation accuracy.

In the initial alignment of the strapdown inertial navigation system, it is difficult to obtain sufficient data information to make the optimized state of convergence in a short time, which cannot realize fast alignment, and the accuracy of the alignment results is poor. The dynamic sliding window is difficult to recover after marginalization, and the linearization points of the Jacobi matrix are inconsistent, leading to the zero-space variation of the information matrix. To address the above problems, we propose an inertial alignment algorithm for sliding window reverse optimization delayed marginalization (RODM) to improve the rapidity and accuracy of the alignment algorithm. The method of storing gyro and accelerometer data during the alignment process and repeatedly solving the stored inertial data using forward and reverse navigation solving and optimization algorithms (OAMs) is used as a way to extend the interval of inertial data collected in a short period of time so that there is enough inertial data to enable the convergence of the state variables of the sliding-window optimization for a short period of time to achieve fast alignment. A delayed marginalization factor is used to update the marginalized prior information, solving the problem that the linearization of the Jacobi matrix must be fixed point to improve the accuracy of the alignment algorithm. We propose an algorithm to improve the accuracy of alignment, which we have verified the rapidity and accuracy of our initial alignment system in the turntable test, where the accuracy of the alignment heading angle reaches 0.136467 mil in 3 min, providing accurate attitude information for navigation and localization.

The visual–inertial odometry (VIO) system is not robust enough in long time operation. Especially, the visual–inertial and Global Navigation Satellite System (GNSS) coupled system is prone to dispersion of system position information in case of failure of visual information or GNSS information. To address the above problems, this paper proposes a tightly coupled nonlinear optimized localization system of RGBD visual, inertial measurement unit (IMU), and global position (GDVI-Fusion) to solve the problems of insufficient robustness of carrier position estimation and inaccurate localization information in environments where visual information or GNSS information fails. The preprocessing of depth information in the initialization process is proposed to solve the influence of an RGBD camera by lighting and physical structure and to improve the accuracy of the depth information of image feature points so as to improve the robustness of the localization system. Based on the K-Nearest-Neighbors (KNN) algorithm, to process the feature points, the matching points construct the best geometric constraints and eliminate the feature matching points with an abnormal length and slope of the matching line, which improves the rapidity and accuracy of the feature point matching, resulting in the improvement of the system’s localization accuracy. The lightweight monocular GDVI-Fusion system proposed in this paper achieves a 54.2% improvement in operational efficiency and a 37.1% improvement in positioning accuracy compared with the GVINS system. We have verified the system’s operational efficiency and positioning accuracy using a public dataset and on a prototype.

Dual-axis rotation can modulate constant and low-frequency errors of inertial sensors; however, low-frequency platform motion introduces dynamic lever-arm velocity into GNSS-aided alignment measurements. We propose a rapid initial alignment framework that integrates encoder-aided compensation for mechanism-induced angular velocity, multi-axis dominant frequency estimation, phase-preserving robust harmonic regression, dynamic lever-arm velocity transfer, period-matched velocity/position integral measurements, and component-wise robust filtering. The frequency estimator employs reliability gating to handle competing non-harmonic spectral peaks and propagates reconstruction quality into the measurement covariance. In static eight-position experiments, the peak-to-peak variation in heading repeatability ranged from 15.12 arcseconds to 52.20 arcseconds. In vehicle-mounted experiments against a known 50 mil heading reference, compared with a conventional error-state Kalman filter using velocity/position matching, the proposed method reduced the heading RMSE from 62.3 arcseconds to 27.3 arcseconds and the within-series peak-to-peak spread from 149.9 arcseconds to 65.1 arcseconds, both by about 56%. In the shipborne experiment, the dominant component of the joint attitude spectrum of the moored vessel was identified at 0.0430 Hz; with identical hardware, identical data, and an identical navigation solver, the proposed alignment method reduced the horizontal position RMSE of 15-day pure inertial navigation from 1033.02 m to 437.23 m (a 57.7% reduction) and the maximum horizontal error from 3438.69 m to 1011.30 m compared with the conventional alignment algorithm.