Search Results (2,338)

Accurate pose estimation is crucial for reliable docking and recovery of Autonomous Underwater Vehicles (AUVs). Traditional visual-based pose estimation methods face inherent challenges: monocular methods often struggle with depth inference, and conventional Perspective-n-Point (PnP) algorithms exhibit accuracy degradation at large viewing angles and limited noise resistance, while binocular systems involve higher computational complexity. This paper proposes a two-stage algorithm that combines iterative PnP initialization with binocular constraint optimization. By using iterative PnP to establish reliable initial estimates, the approach avoids convergence difficulties of direct binocular optimization, while the subsequent binocular refinement leverages stereo geometric constraints to enhance accuracy. Comprehensive evaluation through simulation, land-based experiments, and underwater validation demonstrates consistent performance improvements over conventional geometric methods. In simulation experiments across $-60°$ to $60°$ yaw angles, the method achieves 93.2% and 28.6% improvements in translation and rotation accuracy respectively compared to iterative PnP. Land-based validation confirms 32.7% average rotation error reduction, while underwater experiments demonstrate 76.5% average distance error reduction under real optical conditions including refraction and light attenuation. The method maintains real-time processing capability (2.16 ms per frame), offering a practical solution for AUV pose estimation in docking applications. Full article

This study presents a real-time framework for generating two-dimensional (2D) orthomosaic maps directly from UAV video. The method targets operational scenarios in which a continuously updated 2D overview is required during flight or immediately after landing, without relying on time-consuming offline photogrammetry workflows such as structure-from-motion (SfM) and multi-view stereo (MVS). The proposed procedure incrementally registers sparsely sampled video frames on standard CPU hardware using classical feature-based image registration. Each selected frame is converted to grayscale and processed under a fixed keypoint budget to maintain predictable runtime. Tentative correspondences are obtained through descriptor matching with ratio-test filtering, and outliers are removed using random sample consensus (RANSAC) to ensure geometric consistency. Inter-frame motion is modeled by a planar homography, enabling the mapping process to jointly account for rotation, scale variation, skew, and translation that commonly occur in UAV video due to yaw maneuvers, mild altitude variation, and platform motion. Sequential homographies are accumulated to warp incoming frames into a global mosaic canvas, which is updated incrementally using lightweight blending suitable for real-time visualization. Experimental results on three UAV video sequences with different durations, flight patterns, and scene targets report representative orthomosaic-style outputs and per-step CPU runtime statistics (mean, 95th percentile, and maximum), illustrating typical operating behavior under the tested settings. The framework produces visually coherent orthomosaic-style maps in real time for approximately planar scenes with sufficient overlap and texture, while clarifying practical failure modes under weak texture, motion blur, and strong parallax. Limitations include potential drift over long sequences and the absence of ground-truth references for absolute registration-error evaluation. Full article

Accurate prediction of asphalt mixture density is critical for quality control in pavement engineering. This study develops a novel dielectric-based predictive framework by applying the Lichtenecker–Rother (L-R) dielectric mixing theory to asphalt composites. The model’s key microstructural parameter, the geometric arrangement factor c, was optimized to 0.3 using a combined experimental dataset: laboratory measurements on AC (asphalt concrete) mixtures produced in this study, supplemented with published data from open-graded friction course (OGFC), stone mastic asphalt (SMA), and asphalt mixture (AM) types reported in the literature. The resulting model, termed the Geometric Arrangement Optimization (GAO) model, was systematically compared against three established dielectric models: the complex refractive index method (CRIM), the Rayleigh mixing model, and the Bottcher-type model adapted by Leng et al. (denoted ALL). Validation on a total of 34 sets of laboratory specimens showed that GAO achieved the highest prediction accuracy, with a mean relative error of 1.83% and a coefficient of determination R² of 0.91. When tested on eight independent field cores, GAO maintained reliable performance, yielding a mean relative error of 3.01%. These results indicate that the GAO model provides a physically grounded and practically applicable approach for asphalt mixture density estimation, contributing a useful tool for pavement performance evaluation and quality assurance. Full article

To address the insufficient reliability of manned submersible navigation systems in complex deep-sea environments, this paper proposes an adaptive fusion navigation method based on multi-dimensional confidence assessment. This study proposes a method establishing a four-dimensional evaluation framework for the USBL (Ultra-Short Baseline) positioning system. The framework encompasses signal quality, geometric precision, environmental attenuation, and data stability. It enables the quantitative, real-time assessment of system reliability. Consequently, it facilitates an adaptive weight adjustment mechanism. Experimental results demonstrate that under harsh conditions featuring jump point anomalies and data loss, the proposed algorithm achieves an average position error of 1.15 m. This represents a 53.1% improvement over conventional methods, with the enhancement reaching 58.9% in scenarios specifically affected by jump points. The proposed method study effectively enhances the navigation reliability of manned submersibles in complex underwater acoustic environments, thereby demonstrating significant engineering application value. Full article

This study examines the confined compressive strength (Fcc) of circular, square, and rectangular column geometries under varying confinement conditions. Results indicate that circular columns have the highest Fcc values, exceeding those of square and rectangular shapes. Increased confinement through clamps significantly enhances compressive strength. Five machine learning models, Linear Regression, Decision Tree, Random Forest, AdaBoost, and Gradient Boosting, were used to predict Fcc based on geometric and confinement parameters. Linear Regression and Decision Tree models achieved moderate predictive performance, with R² values of 0.84 and 0.83, respectively, and relatively higher error measures (RMSE, MAE, and MAPE), indicating limited ability to capture complex nonlinear relationships in the data. In contrast, ensemble-based methods demonstrated superior performance. The Random Forest model improved the coefficient of determination to 0.90 while substantially reducing all error metrics, reflecting enhanced generalization through bagging. The boosting-based approaches yielded the best results, with AdaBoost achieving the highest R² value of 0.99 and the lowest RMSE, MAE, and MAPE among all models, followed closely by Gradient Boosting with an R² of 0.98. These results confirm that ensemble learning techniques, particularly boosting algorithms, yield more accurate and robust predictions than single learners for the problem studied. Data visualization techniques, including Regression Error Characteristic curves (REC) and SHapley Additive exPlanations (SHAP) value analysis, highlighted model performance and feature importance, emphasizing the roles of confinement and geometry in compressive strength. This research demonstrates the potential of machine learning to optimize structural engineering design and suggests further exploration of alternative shapes and confinement strategies to enhance structural integrity. Full article

Fall-from-height fatalities in underground construction are closely associated with formwork scaffold operations, where dense steel members cause severe non-line-of-sight (NLOS) and multipath effects that degrade positioning performance. Although ultra-wideband (UWB) technology offers high theoretical ranging accuracy, its deployment-dependent performance in metal-rich scaffold environments remains insufficiently quantified. This study focuses on physical deployment optimization rather than algorithmic compensation. A full-scale formwork scaffold was constructed, and a stepwise one-factor controlled experimental design was employed to quantify the effects of anchor height (H) and horizontal spacing (S) on 3D positioning accuracy. The results show that sub-meter accuracy can be achieved through appropriate deployment, with a minimum 3D RMSE of 0.317 m and over 80% of single-axis errors confined within a 0.2 m engineering-valid region. For this specific setup, the optimal S = 1.5 m correlates with the scaffold grid size (approximately 0.8 times the 1.8 m bay width). While we hypothesize this ratio dependency applies to other geometries, this remains a site-specific observation requiring future cross-validation. Further analysis indicates that this deployment balances vertical signal visibility and multipath suppression. In addition, while the Position Dilution of Precision (PDOP) metric reflects geometric sensitivity, it does not linearly correlate with actual positioning errors under coplanar UWB deployments. These findings provide a rigorous static error model, serving as a critical prerequisite for developing robust real-time safety monitoring systems in scaffold-intensive construction environments. Full article

The use of a corrugated steel plate shear wall (CSPSW) lateral load-bearing system in a steel moment frame (SMF) significantly increases the system’s energy absorption and stiffness. However, the design of CSPSWs involves many parameters and details that greatly increase the complexity of the structure’s response. This study aims to evaluate the effectiveness of the geometric parameters of this system using modern optimization algorithms and an alternative mathematical technique, Response Surface Methodology (RSM). Five geometric parameters, namely crest width (a), diagonal section width (b), corrugation depth (c), sheet thickness (t), and aspect ratio of plate dimension (d), were analyzed to improve the performance of CSPSWs. Design of experiments (DOE) was performed using Design-Expert software, and the required response surface methodology models were designed based on the dimensions of the five variables. Structure weight per meter reduction was set as the optimization goal of the problem. The problem constraints were also defined based on an increase in load-bearing capacity and a reduction in the equivalent plastic strain (PEEQ) percentage in three safety levels 80%, 85% and 90%. Subsequently, the alternative equations developed by RSM to define the objective function and nonlinear constraints were also optimized using modern algorithms in MATLAB 2015. Results revealed a coefficient of determination (R²) of 0.9995 between the experimental and numerical findings and a 1% error between the values obtained from the optimization and reanalysis of the finite elements. Also, they showed an increase in the frame’s lateral load-bearing capacity with the CSPSW, along with a reduction in weight. Full article

High-fidelity 3D reconstruction of human-sized objects typically requires multi-sensor scanning systems that are expensive, complex, and rely on proprietary hardware configurations. Existing low-cost approaches often rely on handheld scanning, which is inherently unstructured and operator-dependent, leading to inconsistent coverage and variable reconstruction quality. This limitation necessitates the need for a controlled, repeatable, and affordable scanning method that can generate high-quality data without requiring multi-sensor hardware or external tracking markers. This study presents a marker-less scanning platform designed for human-scale reconstruction. The system consists of a single structured-light sensor mounted on a vertical linear actuator, synchronised with a motorised turntable that rotates the subject. This constrained kinematic setup ensures a repeatable cylindrical acquisition trajectory. To address the geometric ambiguity often found in vertical translational symmetry (i.e., where distinct elevation steps appear identical), the system employs a sensor-assisted initialisation strategy, where feedback from the rotary encoder and linear drive serves as constraints for the registration pipeline. The captured frames are reconstructed into a complete model through a two-step Iterative Closest Point (ICP) procedure that eliminates the vertical drift and model collapse (often referred to as “telescoping”) common in unconstrained scanning. To evaluate system performance, a modular anthropometric benchmark object representing a human-sized target (1.6 m) was scanned. The reconstructed model was assessed in terms of surface coverage and volumetric fidelity relative to a CAD reference. The results demonstrate high sampling stability, achieving a mean surface density of $0.760{\mathrm{points}/\mathrm{mm}}^2$ on front-facing surfaces. Geometric deviation analysis revealed a mean signed error of −1.54 mm ($\sigma =$ 2.27 mm), corresponding to a relative volumetric error of approximately 0.096% over the full vertical span. These findings confirm that a single-sensor system, when guided by precise kinematics, can mitigate the non-linear bending and drift artefacts of handheld acquisition, providing an accessible yet rigorously accurate alternative to industrial multi-sensor systems. Full article

To achieve Level 4 and above autonomous driving, vehicle localization must remain accurate and reliable under diverse real-world conditions, including complex traffic scenarios, environmental changes, and partial sensor failures. Conventional localization approaches primarily rely on geometric consistency among multi-sensor observations, which can produce physically or contextually implausible pose estimates when sensor reliability degrades or observations become ambiguous. This paper proposes a semantic localization framework that integrates ontology-based semantic reasoning directly into the localization process. The proposed approach reformulates localization as a context-aware constraint selection problem guided by semantic consistency among objects, places, and vehicle poses. By evaluating logical and contextual validity at the hypothesis generation stage, semantically invalid pose hypotheses are eliminated early, and only situation-appropriate semantic constraints are selectively applied during optimization. As a result, compared to the localization system without semantic rules, the proposed framework achieves an average reduction of approximately 35.6% in mean localization error and 47.0% in maximum localization error across both longitudinal and lateral directions. Specifically, the framework supports structured multi-sensor fusion by selectively using sensor information semantically relevant to the driving context. Through this semantics-driven hypothesis reduction, the system reduces computational complexity while enhancing localization robustness and accuracy, particularly under sensor degradation and dynamic environmental conditions. The design of the semantic reasoning structure is also adaptable to cooperative perception scenarios, such as V2V-based information sharing. Full article

Generative pre-training via neural rendering has become a cornerstone for scaling 3D perception in autonomous driving. However, prevalent approaches relying on implicit Neural Radiance Fields (NeRFs) face two fundamental limitations: the shape-radiance ambiguity inherent in vision-centric optimization and the prohibitive computational overhead of volumetric ray marching. To address these challenges, we propose AES-Gaussian, a novel multi-modal pre-training framework grounded in the efficient 3D Gaussian Splatting (3DGS) representation. Diverging from symmetric fusion paradigms, our core innovation is an Asymmetric Encoder architecture that couples a deep semantic vision backbone with a lightweight, physics-aware LiDAR branch. In this framework, LiDAR data serve not merely for semantic extraction, but as sparse physical anchors. By employing a novel Explicit Feature Synergy mechanism, we directly inject raw LiDAR intensity and depth priors into the Gaussian decoding process, thereby rigidly constraining scene geometry in open-world environments. Extensive empirical validation on the nuScenes dataset demonstrates the superiority of our approach. AES-Gaussian achieves state-of-the-art transfer performance, yielding a substantial 7.0% improvement in NDS for 3D Object Detection and a 4.8% mIoU gain in 3D semantic occupancy prediction compared to baselines. Notably, our method reduces geometric reconstruction error by over 50% while significantly improving training and inference efficiency, attributed to the streamlined asymmetric design and rapid Gaussian rasterization. Ultimately, by enhancing both perception accuracy and system efficiency, this work contributes to the development of safer and more reliable autonomous driving systems. Full article

Pipeline integrity is crucial for ensuring the safe and efficient transportation of hydrocarbons. One of the essential methods for maintaining pipeline integrity is periodic inspection using Pipeline Inspection Gauges (PIGs). These PIGs traverse extensive pipeline networks, collecting critical data related to inertial navigation and inspection technologies, such as geometric, ultrasonic, or magnetic flux inspection. Following an inspection, data is downloaded for post-processing to identify and accurately locate pipeline anomalies. Accurate positioning of indications is crucial for effective repair or maintenance of the identified pipeline section. Thus, ongoing efforts aim to improve the precision of indication positioning. This study introduces an innovative method and model for deriving pipeline trajectory characteristics to enhance positioning accuracy. The method is based on distance sampling of odometers, improving the PIG displacement measurement by implementing multiple odometries. Using the method described in this work can compensate for odometer slip, since the distance measurement error was reduced from 15.67% to 1.38%. The model simulates (three and four) odometer trajectories in curvature and calculates the curvature along the pipeline based on odometer data. The curvature model is evaluated with real data obtained from a test circuit, demonstrating that the proposed method and model technique can yield trajectory characteristics such as curvature detection; we can differentiate linear sections from bend sections in the test circuit. However, the curvature measurement error remains considerable due to odometer slippage. Therefore, future work proposes using additional odometers to improve measurement accuracy. Full article

Accurate extraction of the centroid axes of beams with variable cross-sections is critical for infrastructure health monitoring. While 3D laser scanning provides dense point clouds, existing methods face challenges due to fixed slicing directions, sparse or incomplete boundaries, and inaccurate centroid calculations for concave sections. This study proposes a robust framework to overcome these issues. An improved k-d tree ordering algorithm enhances boundary extraction through starting point constraint strategy and dynamic isolated noise point removal mechanism. A ray casting-based boundary-constrained Delaunay triangulation centroid calculation algorithm accurately computes centroids for arbitrary shapes, including concave profiles. An innovative convex hull centroid-driven adaptive normal iterative slicing method dynamically adjusts orientation using historical centroid data, aligning with the local member axis to minimize errors in variable or deformed regions. Experimental validation shows the method outperforms traditional fixed-direction slicing in effectiveness, parameter sensitivity, and deformation robustness, achieving sub-millimeter accuracy. Applied to monitor ultra-high-performance concrete cantilever beams at the Shanghai Grand Opera House, it produced centroid axis data consistent with total station measurements (differences within ±1.2 mm), supporting phased deformation warnings and safety assessments. This work provides a systematic, high-precision solution for extracting geometric axes from complex structural point clouds. Full article

Physical-layer security (PLS) provides an information-theoretic framework for securing wireless communications by exploiting channel and signal-structure asymmetries, thereby avoiding reliance on computational hardness assumptions. Within this setting, lattice codes and their algebraic constructions play a central role in achieving secrecy over Gaussian and fading wiretap channels. This article offers a comprehensive survey of lattice-based wiretap coding, covering foundational concepts in algebraic number theory, Construction A over number fields, and the structure of modular and unimodular lattice families. We review key secrecy metrics, including secrecy gain, flatness factor, and equivocation, and consolidate classical and recent results to provide a unified perspective that links wireless-channel models with their underlying algebraic lattice structures. In addition, we review a newly proposed family of p-modular lattices in Khodaiemehr, H., 2018 constructed from cyclotomic fields $\mathbb{Q}\left({\zeta }_p\right)$ for primes $p\equiv 1(mod4)$ via a generalized Construction A framework. We characterize their algebraic and geometric properties and establish a non-existence theorem showing that such constructions cannot be extended to prime-power cyclotomic fields $\mathbb{Q}\left({\zeta }_{p^n}\right)$ with $n>1$. Finally, motivated by the fact that these p-modular lattices naturally yield mixed-signature structures for which classical theta series diverge, we integrate recent advances on indefinite theta series and modular completions. Drawing on Vignéras’ differential framework and generalized error functions, we outline how modularly completed indefinite theta series provide a principled analytic foundation for defining secrecy-relevant quantities in the indefinite setting. Overall, this work serves both as a survey of algebraic lattice techniques for PLS and as a source of new design insights for secure wireless communication systems. Full article

With the rapid evolution of Digital Twins and Embodied AI, achieving fast, dense, and high-precision 3D perception in unknown environments has become paramount. However, existing Visual SLAM paradigms face a critical dilemma: geometry-based methods often fail in texture-less areas due to feature scarcity, while learning-based approaches frequently suffer from scale drift and unphysical deformations. To bridge this gap, we propose VGGT-Geo, a novel SLAM system that synergizes generative priors from Large Foundation Models with multi-modal geometric optimization. Distinguishing itself from simple cascaded architectures, we construct a Probabilistic Geometric Fusion framework, consisting of (1) Generative Warm-start, leveraging the holistic scene understanding capabilities of the VGGT, (2) Confidence-Aware Optimization to extract dense features via DINOv3 and predict their confidence map, and (3) a Multi-Modal Constraint Closure that fuses point-line features and metric depth priors to constrain rotational Degrees of Freedom in Manhattan Worlds. We conducted systematic evaluations on TUM, Replica, Tanks and Temples, and a challenging self-collected dataset featuring extreme lighting and texture-less walls. Experimental results demonstrate that VGGT-Geo exhibits superior robustness and accuracy in unseen environments. On our most challenging dataset, it achieves an Absolute Trajectory Error of 4–5 cm and a Relative Rotation Error of 0.79°, outperforming current state-of-the-art methods by approximately 50% in trajectory accuracy. This study validates that synergizing the intuition of Large Foundation Models with geometric rigor is a viable path toward next-generation robust SLAM. Full article

Improving the identification accuracy of geometric errors in five-axis machine tools is a critical requirement in advanced manufacturing. Although rotary axes enhance machining flexibility and productivity, they introduce additional geometric errors, among which position-independent geometric errors (PIGEs) are a dominant source of accuracy degradation. Existing studies have paid limited attention to how excitation projection associated with test trajectories affects identification accuracy. This study proposes a trajectory-based identification method using a single setup and systematically investigates the influence of excitation projection on the identification accuracy of rotary-axis PIGEs. An error model and a double differential identification scheme are developed and validated through simulation and experimental studies. For the AC-type machine tool, both simulation and experimental results demonstrate that the accurate identification of all rotary-axis PIGEs is achieved after the second differential under a favorable excitation projection. In contrast, the simulation results for a BC-type machine tool indicate that the optimal excitation projection differs due to its kinematic configuration. Compensation results further confirm the effectiveness of the identified PIGEs, showing a significant reduction in trajectory errors. The results reveal that identification accuracy is governed by the relationship between excitation projection and machine tool structural configuration rather than by the physical test trajectory itself. The proposed method, which requires only a single setup, provides an effective and practical approach for improving the identification accuracy of rotary-axis PIGEs in five-axis machine tools. Full article