Subsurface structural distribution can be detected using Ground-Penetrating Radar (GPR). The distribution can be considered as road fingerprints for vehicle positioning. Similar to the principle of visual image matching for localization, the position coordinates of the vehicle can be calculated by matching real-time GPR images with pre-constructed reference GPR images. However, GPR images, due to their low resolution, cannot extract well-defined geometric features such as corners and lines. Thus, traditional visual image processing algorithms perform inadequately when applied to GPR image matching. To address this issue, this paper innovatively proposes a GPR image matching and localization method based on a novel feature descriptor, termed as central dense structure context (CDSC) features. The algorithm utilizes the strip-like elements in GPR images to improve the accuracy of GPR image matching. First, a CDSC feature descriptor is designed. By applying threshold segmentation and extremum point extraction to the GPR image, stratified strip-like elements and pseudo-corner points are obtained. The pseudo-corner points are treated as the centers, and the surrounding strip-like elements are described in context to form the GPR feature descriptors. Then, based on the feature description method, feature descriptors for both the real-time image and the reference image are calculated separately. By searching for the nearest matching point pairs and removing erroneous pairs, GPR image matching and localization are achieved. The proposed algorithm was evaluated on datasets collected from urban roads and railway tracks, achieving localization errors of 0.06 m (RMSE) and 1.22 m (RMSE), respectively. Compared to the traditional Speeded Up Robust Features (SURF) visual image matching algorithm, localization errors were reduced by 86.6% and 95.7% in urban road and railway track scenarios, respectively.

One of the predominant technologies for multi-source navigation in vehicles involves the fusion of GNSS/IMU/ODO through a factor graph. To address issues such as the asynchronous sampling frequencies between the IMU and ODO, as well as diminished accuracy during GNSS signal loss, we propose a GNSS/IMU/ODO integrated navigation method based on an adaptive sliding window factor graph. The measurements from the ODO are utilized as observation factors to mitigate prediction interpolation errors associated with traditional ODO pre-integration methods. Additionally, online estimation and compensation for both installation angle deviations and scale factors of the ODO further enhance its ability to constrain pose errors during GNSS signal loss. A multi-state marginalization algorithm is proposed and then utilized to adaptively adjust the sliding window size based on the quality of GNSS observations, enhancing pose optimization accuracy in multi-source fusion while prioritizing computational efficiency. Tests conducted in typical urban environments and mountainous regions demonstrate that our proposed method significantly enhances fusion navigation accuracy under complex GNSS conditions. In a complex city environment, our method achieves a 55.3% and 29.8% improvement in position and velocity accuracy and enhancements of 32.0% and 61.6% in pitch and heading angle accuracy, respectively. These results match the precision of long sliding windows, with a 75.8% gain in computational efficiency. In mountainous regions, our method enhances the position accuracy in the three dimensions by factors of 89.5%, 83.7%, and 43.4%, the velocity accuracy in the three dimensions by factors of 65.4%, 32.6%, and 53.1%, and reduces the attitude errors in roll, pitch, and yaw by 70.5%, 60.8%, and 26.0%, respectively, demonstrating strong engineering applicability through an optimal balance of precision and efficiency.

Using the Global Navigation Satellite System (GNSS), it is difficult to provide continuous and reliable position service for vehicle navigation in complex urban environments, due to the natural vulnerability of the GNSS signal. With the rapid development of the sensor technology and the reduction in their costs, the positioning performance of GNSS is expected to be significantly improved by fusing multi-sensors. In order to improve the continuity and reliability of the vehicle navigation system, we proposed a multi-sensor tight fusion (MTF) method by combining the inertial navigation system (INS), odometer, and barometric altimeter with the GNSS technique. Different fusion strategies were presented in the open-sky, insufficient satellite, and satellite outage environments to check the performance improvement of the proposed method. The simulation and real-device tests demonstrate that in the open-sky context, the error of sensors can be estimated correctly. This is useful for sensor noise compensation and position accuracy improvement, when GNSS is unavailable. In the insufficient satellite context (6 min), with the help of the barometric altimeter and a clock model, the accuracy of the method can be close to that in the open-sky context. In the satellite outage context, the error divergence of the MTF is obviously slower than the traditional GNSS/INS tightly coupled integration, as seen by odometer and barometric altimeter assisting.

Magnetic positioning is a promising technique for vehicles in global navigation satellite system (GNSS)-denied scenarios. In general, a fixed-length magnetic sequence is required to provide an initial positioning result, which means that users need to wait a relatively long distance. To minimize this initialization distance, a map-aided fast initialization method, including magnetic database construction and magnetic positioning, is proposed in this paper. For magnetic database construction, a multisource fused database is established using a precise and effective strategy in which the positions of reference points (RPs) and the diverse information of paths are obtained from the map and the magnetic field is calculated using data collected during driving. For magnetic positioning, we innovatively propose a coarse–fine combination method that improves the positioning accuracy within a short distance. In the coarse map matching stage, by detecting the vehicle’s motion and utilizing the topological relationships between paths, the search range is precisely narrowed. In the fine magnetic localization stage, an improved mean absolute difference (MAD) metric and a derivative metric are combined to form a joint matching criterion to determine the positioning result. The experimental results illustrate the importance of each module in the proposed method, which improves the precision of the database up to 80% and significantly shortens the initialization distance up to 50%.

Magnetic navigation is a promising positioning technique for scenarios where a global navigation satellite system (GNSS) is unavailable, such as for underwater submarines and aircraft in space. For ground scenarios, it faces more challenges, since the magnetic distribution suffers interference from surrounding objects such as buildings, bridges, and vehicles. It is natural to think how feasible it is to apply magnetic matching positioning to vehicles. In this paper, a theoretic distribution model is proposed to analyze the magnetic field around objects such as buildings, bridges, and vehicles. According to the experiments, it is shown that the proposed model matches the experimental data well. In addition, a comprehensive indicator metric is defined in this paper to describe the feasibility of the magnetic matching method based on the statistical characteristics of magnetic maps. The best length of matching window, anti-noise performance, and pre-comparison of positioning accuracy in different regions can be easily derived using the proposed comprehensive indicator metric. Finally, the metric is verified through a drive test using different building densities.

Geomagnetic matching positioning is a promising autonomous positioning technique that relies on the Earth's magnetic field. The Cramer-Rao lower bound (CRLB) is commonly employed to assess positioning accuracy of various positioning technologies, and it can formalize the main factors that affect positioning accuracy. This paper establishes a refined error model for geomagnetic matching that accounts for multiple sources of error, based on which the CRLB for geomagnetic matching is derived. Building on this, a geomagnetic matching method is proposed, which achieves performance close to the theoretical optimal accuracy. The method accomplishes geomagnetic matching by fitting the Weighted Mean Square Difference (WMSD) curve/surface. Monte Carlo simulations demonstrate that the CRLB effectively characterizes the optimal accuracy of geomagnetic matching. In addition, CRLB can serve as a practical criterion to guide magnetic sensor selection, area suitability assessment, and the configuration of the matching window.