In the extraction of parallelism and warping data for large-scale, high-density ice-making pipes, issues such as low detection accuracy and incomplete data coverage are prevalent. This paper proposes a method based on bundle adjustment for regional networks, utilizing a 3D laser acquisition approach with multi-prism target spheres. The target sphere centers are extracted using a radius-constrained random sample consensus (RANSAC) sphere fitting method. By applying coordinate transformations between the absolute coordinates of the station point cloud origins, the relative coordinates of the target centers, and the absolute coordinates of the target centers, a global solution for the positions and orientations of all stations is achieved using bundle adjustment. The method was validated using scan data from the National Speed Skating Oval. The results show that after point cloud matching, the internal consistency accuracy is 2.6 mm, and the external consistency accuracy is 1.9 mm, demonstrating higher acquisition accuracy compared to existing methods.

Using a measurement tower to convert underwater positioning to above-water positioning is the main method for positioning submarine tunnel segments at home and abroad. However, the coupled effects of measurement tower deformation and segment deformation affect positioning accuracy and are unable to adapt to deep-water docking. This article proposes an underwater active light encoding cooperative target photogrammetry segment docking positioning method, which uses active light to increase the optical distance, suppress backscattering, and encode the target to overcome the influence of suspended particles and plankton. Combined transmission light separation imaging and refractive index as unknown parameter measurement adjustment, overcoming the influence of water body turbidity and refractive index induced optical distortion on underwater photogrammetry. A photogrammetry system is installed on the top of the approaching segment's docking end, and a cooperative target is installed at the corresponding position of the already-submerged segment to ensure measurement and determine its position and attitude in the construction coordinate system. The position of the approaching segment in the construction coordinate system is obtained through rear intersection calculation, and the positional and attitude adjustment information of the segment is generated by comparing it with the theoretical position it needs to be sunk to, which assists in docking. The application of this method in the Deep Channel and Dalian Bay Submarine Tunnel projects shows that the segment docking linear accuracy reaches 2 cm and 100 m, providing key technical support for the construction of submarine tunnels under deep-water conditions in the future.

With the arrival of the intelligent safety monitoring era, research on monitoring point positioning measurement in tunnels and mines is gradually developing towards all-time and all-weather aspects. In response to the problems of poor real-time performance and long measurement period in tunnel monitoring point positioning, as well as susceptibility to factors such as dust and lighting, this paper introduces millimeter wave radar with high distance and speed resolution to conduct high-precision positioning research for tunnel monitoring points. An innovative millimeter-level positioning method for tunnel environment based on multiple millimeter wave radars network is proposed. Firstly, based on the traditional fast Fourier transform to extract ranging information, a method is proposed to refine the frequency spectrum using chirp-Z transform. This method can optimize the ranging observations and ensure ranging accuracy at the millimeter level. Secondly, due to the drawback that traditional ranging radars can only obtain one-dimensional radial deformation, multiple millimeter radars network method is introduced. Further, a functional model for multi machine network positioning is established. In addition, a random model is proposed that takes into account the difference of each millimeter wave radar in radar pulse observation accuracy of each epoch and prior distance. Finally, the precision of ranging and positioning is verified through tunnel tests. The results show that, the phase difference method based on chirp-Z transform proposed in this manuscript can achieve ranging precision within 0.3 mm, and the computational efficiency of the algorithm is improved by 50 times compared to the existing method. When the target to be monitored is stable, the precision of X, Y and Z directions is 2.7 mm, 0.6 mm and 6.6 mm. However, the elevation direction precision is slightly lower due to the influence of tunnel height. In the case of micro movement of the target to be tested, this proposed method can detect small deformations. The method proposed in this manuscript meets the requirements of monitoring point real-time positioning in tunnel environment for all time, high accuracy, and long periods. Furthermore, it is expected to be applied in industrial structure deformation monitoring.

Realistic 3D modeling and digital twins have become essential foundations for bridge operation and management. However, given the complex geometric structures of bridges, current 3D modeling methods face issues such as large amounts of raw data collection, low modeling efficiency, and missing or deformed model details. In response to these challenges, this paper investigates a bridge realistic 3D reconstruction method based on 3D Gaussian radiance fields. This method utilizes 3D Gaussian functions to construct a Gaussian radiance field from sparse point clouds generated by captured images. Adaptive optimization of radiance field parameters is performed based on stochastic gradient descent, and real-time visualization of the 3D model is achieved through differentiable rasterization, resulting in high-quality bridge 3D reconstruction and rendering. The study explores the impact of different image resolutions and various parameter changes on bridge modeling. Comparisons with traditional methods are made to provide theoretical and technical support for further bridge applications, promoting efficient and accurate realistic 3D reconstruction of complex bridge structures.

In the realm of smart city development, the scalability, privacy, and heightened reliability of CORS (continuously operating reference station) location services have emerged as pivotal focal points. This paper presents an innovative urban CORS augmented positioning service leveraging PPP-RTK (precise point positioning-real-time kinematic) technology. It delineates the formulaic methodology for achieving enhanced precision single-point positioning within urban environments, elucidating the estimable parameters essential for urban CORS augmented positioning and their practical implementation on client platforms. Through integration with real-time data sourced from the Guangzhou CORS network, the efficacy of the PPP-RTK service is rigorously evaluated. Test results demonstrate that the initial epoch of the PPP-RTK client plane rapidly converges to centimeter-level accuracy, with vertical elevation convergence achieved within approximately seven epochs. Furthermore, the performance of the enhanced positioning parameter SSR2OSR (state space representation to observation space representation) in PPP-RTK is found to be comparable to that of short baseline RTK solutions, thus substantiating its capacity to cater to the monitoring requirements of a substantial user base. On-board experiments exhibit superior 3D accuracy, surpassing lane-level precision by a margin of less than 0.5 meters, underscoring the capability of PPP-RTK positioning to fulfill the stringent reliability criteria essential for various positioning applications.

With the construction of transportation networks, the number of completed tunnels and the increasing service life of tunnels have brought great challenges to the safe operation of tunnels. Rapid detection of tunnel lining cracks and accurate extraction of crack length and width characteristics is an important guarantee for achieving efficient maintenance and safe operation of tunnel. This article proposes an efficient and accurate post-processing algorithm for tunnel crack diseases, based on the prediction segmentation mask of DeepLabV3+ semantic segmentation model. The connected domain discrimination refinement algorithm and endpoint clustering instance differentiation algorithm are used to process the mask fracture situation, achieving accurate extraction of tunnel crack skeleton and instance differentiation. Finally, the length calculation and grayscale difference value width classification algorithm are used to calculate the crack length and width characteristics. The accuracy of length and width calculation is 92.2% and 86.3%, respectively.

In response to the issue of mismatch between nominal accuracy and actual measurement accuracy in different application scenarios of the binocular stereo industrial photogrammetry system, in-depth research was conducted on the mesh structure of it. It has been found that if the intersection angle of the measurement points is large than 45°, the horizontal angle between measurement points and baseline among 28°and 68°, vertical viewing angle less than 23 °, and the distance between the measure points and the baseline among 0.4B and 1.3B, the measure points' accuracy is high. If the measure points out of this range, the uncertainty of the points measurement will increase. Based on the above conclusions, a method has been proposed that use a binocular stereo industrial photogrammetry system to measure the length of a scale-bar at specified positions within the measurement field. By comparing the difference between the measured values of the scale-bar and the nominal value, the accuracy of the system's measurement field can be quickly evaluated. Experiments have shown that this method can quickly and accurately evaluate the measurement field accuracy of the binocular stereo industrial photogrammetry system, which plays an important guiding role in ensuring the quality of on-site operations and improving the efficiency of on-site operations.

In view of the low efficiency of the calculation of ultra-high-degree spherical harmonic synthesis for non-equal latitude distributed points of regional area, this article has conducted a deep research on the interpolation algorithm of associated Legendre functions. Combined with the harmonic expansion of the model gravity anomaly, a fast calculation method using interpolation was proposed for ultra-high-degree model free-air gravity anomaly. In order to verify the advantages of this new method over point sets with narrow distribution from east to west, the efficiency of the new method was further improved by adopting the spherical harmonic rotation(SHR) technology. The experimental results showed that the proposed method using interpolation reduced the calculation time from 3 669.41 s to 98.05 s compared with point-by-point approaches, with errors not exceeding ±0.005 mGal, when we used EGM2008 model up to 2160 degree and order to achieve the model free-air gravity anomalies of 30 303 hexagonal grid points at the same height level in Japan with non-equal latitude distribution. Meanwhile, by applying SHR and rotating these points distributed in the north-south direction to the east-west direction, the time consumption for the corresponding solution was further reduced from 98.05 s to 19.06 s, with a speed-up ratio of nearly 200 times to the original method. The method proposed in this article effectively solves the problem of low efficiency in solving ultra-high-degree model free-air gravity anomalies for regional non-equal latitude distributed points, and has a higher computing speed-up effect in the case of east-west elongated distribution.

High-precision satellite attitude control is an important data preprocessing aspect of satellite gravity mission operation. The key payload star trackers onboard the gravity field and steady-state ocean circulation explorer (GOCE) satellite inevitably experience temperature variations in its low orbit, leading to inter-boresight angles (IBA) deviations ranging from 2 to 14 arcseconds, directly impacting the accuracy of satellite attitude. Quantitative analysis of temperature effects on satellite attitude and precise determination of satellite angular velocities are essential steps in the satellite data preprocessing workflow, directly influencing the accuracy of high-precision gravity gradient component reconstruction. In this study, based on the characteristics of the GOCE satellite mission, we develop a temperature effect correction method for joint attitude quaternion reconstruction using multiple star trackers. This method involves constructing a linear function of temperature-related relative attitude offsets between star trackers, establishing a weighted matrix considering the precision differences among sensor axes, and obtaining the optimal quaternion reconstruction of attitude velocities based on the principle of least squares. Additionally, in the original attitude data processing, we propose a logarithmic quaternion Hermite hypersurface interpolation method for data optimization. The research results demonstrate that the corrected attitude quaternions calculated from star tracker data exhibit no significant deviation when compared with reference frame information. Moreover, after temperature effect correction, the noise level of angular velocity for each tracker axis significantly decreases by approximately two orders of magnitude, achieving an accuracy of 10^-10 rad·s^-1 and significantly improving the precision of velocity reconstruction. Additionally, the angular velocity accuracy of each tracker axis maintains good consistency.The power spectral density of the gravity gradient trace calculated based on this method shows a more significant improvement in the whole frequency domain.

The GNSS signal suffers from quality degradation or outages due to blockages in the urban environment, leading to the error divergence of precise point positioning (PPP)/inertial navigation system (INS) tightly coupled system. Although the traditional height constraint models based on the constant height hypothesis can effectively suppress INS error accumulation under flat surface, it is difficult to enhance the PPP/INS tightly coupled model because of unadapting the change of surface under the high occlusion environment. In this paper, considering the similar characteristic of short-term height variation rate during vehicle experiment, an adaptive short-term height variation rate constraint (ASTHVRC) model of PPP/INS tightly coupled system is proposed. The effectiveness of the proposed model is verified by both simulated occlusive environment and real urban environment. In the real urban environment, comparing with the height constraint-free (HCF), the height rate-weighted constant height constraint (HRWHC) and the inter-epoch constant height constraint (ICHC), the results show that ASTHVRC model improves the accuracy of PPP/INS tightly coupled height solution by 52.2%, 49.2%, 70.9%, respectively.

PPP-RTK correction products serve as fundamental information for achieving the satellite navigation-based positioning with high-precision and rapidly convergence simultaneously. Integrity monitoring is a core requirement for ensuring the reliability of PPP-RTK positioning. The traditional integrity monitoring methods for PPP-RTK correction products suffer from the lack of the monitoring risks allocation tree and the low sensitivity in monitoring multiple faults. These shortcomings cause a decreased availability of the PPP-RTK correction products. In this contribution, a vectorized integrity monitoring method is proposed for PPP-RTK correction products. Based on the minimum protection level criteria, the integrity and continuity risk of PPP-RTK correction products has been allocated. A sequential pre- and post-broadcasting loopback monitoring architecture has been developed in the range domain and the position domain, respectively. The proposed method, implemented with carrier phase observations, incorporates vectorized classification and monitoring, along with the executive monitoring the results of multiple stations. The proposed vectorized integrity monitoring method enhances the sensitivity and availability of the integrity monitoring for PPP-RTK correction products. Analysis of simulation and real-world data indicates that the proposed method outperforms traditional methods in both sensitivity and availability, achieving at least 99% of availability.

The meridian arc length formula is usually expressed as a series expansion of the geodesic latitude B, whose coefficients are parameterized by first eccentricity e. In this paper, the formula is rederived using third flattening n and expressed as three forms of trigonometric functions: multiple angle form, exponential form, and double dangle form.The denominator value in each coefficient in the rederived formula is obviously smaller, and the individual higher-order term coefficients disappear, with a simple structure and concise form. Based on this, the truncation error analysis of the 8th-order and 10th-order expanded formulas of the three representations shows that the accuracy of the 8th-order expanded formulas of the three representations is at least millimeters, which can satisfy the daily use scenarios, while the accuracy of the 10th-order expanded formulas has been improved by at least two orders of magnitude, which can satisfy the high-precision use scenarios. Under the high-precision use scenario, the meridian arc length formulas of different representations have different practicality, and the analysis shows that the exponential form of trigonometric function is recommended at the low latitude of 0°N—30°N, double angle form of trigonometric function is recommended at the middle latitude of 30°N—55°N, and the multiple angle form of trigonometric function is recommended at the high latitude of 55°N—90°N.

Remote sensing images are often contaminated by stripe noise during the acquisition process, which reduces the visual effect of remote sensing images and has an adverse effect on image interpretation and inversion. Although some mainstream stripe noise removal methods based on variational methods can remove stripe noise, they often lead to serious loss of image detail information. Based on the above problems, this paper proposes a remote sensing image stripe noise removal model DISUTV based on detail information constraint. In the DISUTV model, the proposed detail information separation operator based on bilateral filter and orthogonal subspace projection is effectively combined with one-way total variation regularization term, group sparsity regularization term and one-way total variation regularization constraint term, and the alternating direction multiplier method is used to solve it, which is used to obtain high-precision stripe noise without detail information from stripe noise images. The stripe noise removal ability, detail information retention ability and robustness of the algorithm are verified using simulated data and real data, and compared with existing cutting-edge methods. Experimental results show that the proposed method can better retain the detail information of the image while removing stripe noise, and presents good qualitative and quantitative results.

This study presents a hybrid intelligence-based approach, named K-CAPSNet, for extracting knowledge from streetscape images. To tackle the challenge of intelligent extraction of streetscape image objects, we develop a panoramic segmentation network with a joint attention mechanism that integrates both channel information and spatial information of streetscape images. This improves the object segmentation accuracy. Additionally, we incorporate streetscape knowledge, which is formed by people in production and life, into the streetscape image cognition process. We set the object marking threshold using a priori knowledge to optimize the segmentation results. Moreover, we utilize the a priori knowledge of streetscape images to verify the topological relationship between streetscape objects and to mine spatial relationship knowledge using depth information. Finally, we employ semantic templates to describe and express the type, number, and spatial relationship between streetscape objects. The experimental results demonstrate that our method outperforms the baseline network and significantly improves the quality of panoramic segmentation and recognition, thereby achieving better extraction and expression of the knowledge of streetscape images.

The automatic recognition of pedestrian intentions is a difficult issue in location-based services, which is crucial for establishing intelligent navigation services and new human-computer interaction method. Currently, using behavior patterns to estimate pedestrian intentions has become a mainstream solution, but this approach relies on multiple sensors and has time delays. This article proposes a pedestrian intention detection method based on brain imaging technology, which interprets pedestrian turning intentions through multi-channel, high-resolution EEG signals. Firstly, according to the standard motor imagery paradigm, EEG samples corresponding to four types of intentions within road intersection scenes were collected, including straight ahead, stop, left turn, and right turn. Then, by fusing the features of EEG in time-frequency domain, spatial domain, and functional connectivity domain, the spatiotemporal functional connectivity networks (STFCNs) of EEG are constructed to express the process of EEG activity, facilitating the capture of EEG features highly related to the intent. Finally, a graph convolutional neural network was used to encode the STFCNs, completing the mapping from EEG to four types of navigation intentions. The experimental results show that the average accuracy (F₁ score) of detecting four types of intentions using a short time window (1 s) is 0.443±0.062, and the highest accuracy can reach 0.571. The average accuracy with a long time window (6 s) is 0.525±0.084, and the highest accuracy is 0.665. The detection accuracy of this method is slightly better than other classification algorithms, and its detection ability for forward and stop intentions is excellent, up to 0.740 and 0.700.

Supported by deep learning methods for building shape cognition, it has become a hot research topic in fields such as cartography. The feature mining ability of deep learning can help extract embedded representations of shapes, supporting application scenarios such as cartographic generalization and spatial retrieval. A graph convolutional neural network model for building shape classification that integrates global features and graph node features is constructed, and validated using building data as an example. Firstly, a weighted building graph is constructed, and then a fusion description of the shape is generated based on the 4 macroscopic shape features of building and the multi-level local and regional structural features of boundary vertice. Graph convolutional neural networks are used to extract multi-level shape information, and the feature coding generated by fusing graph representations from different layers is used for shape classification.The experimental results show that compared to the comparative method, the proposed method is more effective in distinguishing the shape categories of different buildings, and the generated feature coding have positive shape discrimination.