Autonomous navigation without external GNSS aiding is crucial for some kinds of land vehicle applications, such as military vehicles and unmanned vehicles navigation under GNSS denied environments. A typical solution for autonomous navigation can be achieved by integrating Inertial Navigation System (INS) and odometer, where the odometer can provide velocity aiding for INS. The INS/odometer integration approach can dramatically improve the navigation performances compared with the standalone INS approach, however its positioning error is still gradually accumulating with time because of lacking external position correction. This paper proposes an approach to aid the INS/odometer integration by using vision positioning, where a UAV is used to carry the vision camera and helps to realize the positioning of the vehicle by image matching. The UAV vision positioning acts a role like GNSS and provides constant position correction for INS/odometer integration. A dual-rate Kalman filter is proposed and utilized to realize the data fusing of vision, INS and odometer. Simulation and filed tests show that the proposed approach can dramatically improve the autonomous navigation performances for land vehicles.

High-precision navigation of the underground excavating machine is the core of realizing safe excavating and autonomous control, but it is difficult to realize high-precision navigation for the underground excavating machine. The integration of GNSS and inertial navigation system are generally adopted for the high-precision navigation of the ground equipment, which can provide high-precision navigation for a long time. However, the GNSS signals cannot be used in underground operation, so the navigation scheme applied to the ground cannot be used underground. At the same time, the velocity of the underground excavation machine is very slow and the operation time is very long, which makes it impossible to use pure inertial navigation scheme for navigation. Because the inertial navigation error accumulates with time, if the operation time is too long, the navigation accuracy cannot be guaranteed. The integration of inertial navigation and odometer is considered as a reliable scheme, but simulation and experimental analysis show that the traditional integration of inertial navigation and odometer cannot meet the operation requirements of the underground excavating machine in the presence of serious slipping and measurement noises. This paper proposes an integration of fully damped inertial navigation/odometer to provide long-term high-precision orientation navigation information for mining equipment. The advantage of the fully damped inertial navigation/odometer integration approach is that it can maintain high accuracy of orientation measurement in one week or longer under the condition of sensor drift error and odometer measurement error. Simulation and field tests are carried out to validate the performance of the proposed approach.

Matching remotely sensed multimodal images is a crucial process that poses significant challenges due to nonlinear radiometric differences and substantial image noise. To overcome these difficulties, this study presents a novel and practical template-matching algorithm specifically designed for this purpose. Unlike traditional approaches that rely on image intensity, the proposed algorithm focuses on matching multimodal images based on their geometric structure information. This approach enables the method to effectively adapt to variations in grayscale caused by radiometric differences. To enhance the matching performance, principal component analysis calculation based on the log-Gabor filter is proposed to estimate the structural feature of the image. The proposed method can estimate the structure feature accurately even under severe noise distortion. In addition, a learnable matching network is proposed for similarity measuring to adapt to the gradient reversal caused by the radiometric difference among remotely sensed multimodal images. Infrared, visible light, and synthetic aperture radar images are adopted for the evaluation, to verify the performance of the proposed algorithm. Based on the results, the proposed algorithm has a distinct advantage over other state-of-the-art template-matching algorithms.

A new cross-modal image matching method is proposed to solve the problem that unmanned aerial vehicles (UAVs) are difficult to navigate in GPS-free environment and night environment. In this algorithm, infrared image or visible image is matched with satellite visible image. The matching process is divided into two steps, namely, coarse matching and fine alignment. Based on the dense structure features, the coarse matching algorithm can realize the position update above 10 Hz with a small amount of computation. Based on the end-to-end matching network, the fine alignment algorithm can align the multi-sensor image with the satellite image under the condition of interference. In order to obtain the position and heading information with higher accuracy, the fusion of the information after visual matching with the inertial information can restrain the divergence of the inertial navigation position error. The experiment shows that it has the advantages of strong anti-interference ability, strong reliability, and low requirements on hardware, which is expected to be applied in the field of unmanned navigation.

To tackle the problem of high-accuracy self-calibration for marine dual-axis Rotary Inertial Navigation System (RINS) on moving base, this paper proposes a dynamic self-calibration approach with anti-disturbance capabilities. The primary work is twofold. Firstly, a state space model utilizing the Kalman filter is established, grounded in the error model of Inertial Navigation System (INS) and the calibration model of Inertial Measurement Unit (IMU). Secondly, a disturbance velocity elimination method based on wavelet transform that effectively eliminates disturbance velocity during calibration processes is proposed. Additionally, to tackle the low accuracy associated with boundary data in wavelet transform, a boundary extension method base on Particle Swarm Optimization (PSO) is introduced, which significantly enhances the accuracy of boundary data in wavelet transform. Experimental results indicate that the calibration accuracy of proposed method on static base are basically the same with on moving base. Gyro calibration accuracy is improved by over 92% relative to traditional non-anti-disturbance methods, moreover, other key parameters accuracy exhibits varying degrees of enhancement. Furthermore, a long-term maritime navigation experiment was conducted to validate these results, revealing an improvement in positioning accuracy by 24%, which significantly improved navigation accuracy of RINS under marine conditions.

The startup drift phenomenon that exists in MEMS INSs increases the navigation error, prolonging the start-up time. Aiming to resolve this problem, a startup drift compensation method based on a PSO-GRNN model is proposed in this paper. We adopted a correlation analysis to determine the input parameters of the PSO-GRNN model that mainly affect startup drift. In the process of training this model, we used the PSO algorithm to optimize the spread parameter of the PSO-GRNN model. The information transmission function between particle swarms was used to find the best spread parameter by iterative optimization, the particle’s position was mapped to the GRNN model, and the GRNN model was constructed with the optimal position of the swarm as the spread parameter. This method can effectively compensate for startup drift and improve navigation accuracy. Startup drift compensation experiments were carried out at different ambient temperatures. Compared with the MEMS INS data without compensation, the standard deviation of the MEMS INS data with the proposed method decreased by more than 80.6%, and the peak-to-peak value of the MEMS INS data decreased by over 72.7%. Compared with the traditional method, the standard deviation of the MEMS INS data compensated via this method decreased by 54.5% on average, and the peak-to-peak value decreased by 42.8% on average. Meanwhile, the performance of this method was verified by navigation experiments. With the proposed method, the speed error improved by over 36.4%, and the position error improved by over 41.1%. The above experiments verified that the method of this paper significantly improved navigation performance.