Search Results (114)

This paper addresses the challenge of robust pose estimation for spacecraft under rapid inter-frame motion, proposing a two-stage point cloud registration framework. The first stage computes coarse pose estimation by leveraging Fast Point Feature Histogram (FPFH) descriptors with random sample and consensus (RANSAC) for correspondence matching, effectively handling significant positional displacements. The second stage refines the solution through geometry-aware fine registration using raw point cloud data, enhancing precision through a multi-scale iterative ICP-like framework. To validate the approach, we simulate time-of-flight (ToF) sensor measurements by rendering NASA’s public 3D spacecraft models and obtain 3D point clouds by back-projecting the depth measurements to 3D space. Comprehensive experiments demonstrate superior performance over several state-of-the-art methods in both accuracy and robustness under rapid inter-frame motion scenarios. The dual-stage architecture proves effective in maintaining tracking continuity while mitigating error accumulation from fast relative motion, showing promise for autonomous spacecraft proximity operations. Full article

Rapid urbanization in China has led to an increase in the volume of daily road garbage, posing challenges to municipal sanitation. Automatic garbage collection is thus essential for sustainable management. This paper proposes an improved RT-DETR-based (Real-Time Detection Transformer) detection model, RGD-DETR, to improve road garbage detection performance. Firstly, an improved feature pyramid module that leverages multi-scale feature fusion techniques to enhance feature extraction effectiveness is designed. Secondly, a state space model is introduced to accurately capture long-range dependencies between image pixels with its spatial modeling capability, thus obtaining high-quality feature representation. Thirdly, a Dynamic Sorting-aware Decoder is adopted to embed a dynamic scoring module and a query-sorting module in adjacent decoder layers, enabling the model to focus on high-confidence predictions. Finally, the classification- and localization-oriented loss and matching cost are introduced to improve target localization accuracy. The experimental results on the road garbage dataset show that the RGD-DETR model improves detection accuracy (mAP) by 1.8% compared with the original RT-DETR, performing well for small targets and in occlusion scenarios. Full article

Accurate simulation of fluid flow around vertical cylinders is essential in numerous engineering applications, particularly in the design and assessment of offshore structures, bridge piers, and coastal defenses. This study employs the smoothed particle hydrodynamics (SPH) method to investigate the complex dynamics of breaking waves impacting a vertical pile, a scenario marked by strong free-surface deformation, turbulence, and the wave–structure interaction. The mesh-free nature of SPH makes it especially suitable for capturing such highly nonlinear and transient hydrodynamic phenomena. The primary objective of the research is to evaluate the performance of different SPH dissipation schemes, namely artificial viscosity, laminar viscosity, and sub-particle scale (SPS) turbulence models, in reproducing key hydrodynamic features. Numerical results obtained with each scheme are systematically compared against experimental data to assess their relative accuracy and physical fidelity. Specifically, the laminar + SPS model reproduced the peak horizontal wave force within 5% of experimental values, while the artificial viscosity model overestimated the force by up to 25%. The predicted wave impact occurred at a non-dimensional time of $t/T\approx 0.28$, closely matching the experimental observation. Furthermore, force and elevation predictions with the laminar + SPS model remained consistent across three particle spacings ($dp=0.05\mathrm{m},0.065\mathrm{m},0.076\mathrm{m}$), demonstrating good numerical convergence. This work provides critical insights into the suitability of SPH for modeling wave–structure interactions under breaking wave conditions and highlights the importance of proper dissipation modeling in achieving realistic simulations. The performance of the dissipation schemes remained robust across three tested particle spacings, confirming consistency in force and elevation predictions. Additionally, it underscores the sensitivity of SPH predictions to spatial resolution, highlighting the need for careful calibration to ensure robust and reliable outcomes. The study contributes to advancing SPH as a practical tool for engineering design and hazard assessment in coastal and offshore environments. Full article

Global navigation satellite systems (GNSSs) exhibit significant performance limitations in signal-deprived environments such as indoor spaces and underground spaces. Although visual SLAM has emerged as a viable solution for ego-motion estimation in GNSS-denied areas, conventional approaches remain constrained by static environment assumptions, resulting in a substantial degradation in accuracy when handling dynamic scenarios. The EMS-SLAM framework combines the geometric constraints and semantics of SLAM to provide a real-time solution for addressing the challenges of robustness and accuracy in dynamic environments. To improve the accuracy of the initial pose, EMS-SLAM employs a feature-matching algorithm based on a graph-cut RANSAC. In addition, a degeneracy-resistant geometric constraint method is proposed, which effectively addresses the degeneracy issues of purely epipolar approaches. Finally, EMS-SLAM combines semantic information with geometric constraints to maintain high accuracy while quickly eliminating dynamic feature points. Experiments were conducted on the public datasets and our collected datasets. The results demonstrate that our method outperformed the current algorithms of SLAM in highly dynamic environments. Full article

In practical engineering, the asymmetric problem of the domain label space is inevitable owing to the prior fault information of the target domain being difficult to completely obtain. This implies that the target domain may include unseen fault classes or lack certain fault classes found in the source domain. To maintain diagnostic performance and knowledge generalization across different speeds, cross-domain intelligent fault diagnosis (IFD) models are widely researched. However, the rigid requirement for consistent domain label spaces hinders the IFD model from identifying private fault patterns in the target domain. In practical engineering, the asymmetric domain label space problem is inevitable, as the target domain’s fault prior information is difficult to completely obtain. This means that the target domain may have unseen fault classes or lack some source domain fault classes. To address these challenges, we propose an asymmetric cross-domain IFD method with label position matching and boundary sparse learning (ASY-WLB). It reduces the IFD model’s dependence on domain label space symmetry during transient speed variation. To integrate signal prior knowledge for transferable feature representation, angular resampling is used to lessen the time-varying speed fluctuations’ impact on the IFD model. We design a label-positioning information compensation mechanism and weighted contrastive domain discrepancy, accurately matching unseen class label information and constraining the diagnosis model’s decision boundary from a data conditional distribution perspective. Finally, extensive experiments on two time-varying speed datasets demonstrate our method’s superiority. Full article

Pose measurement of non-cooperative targets in space is one of the key technologies in space missions. However, most existing methods simulate well-lit environments and do not consider the degradation of algorithms in low-light conditions. Additionally, due to the limited computing capabilities of space platforms, there is a higher demand for real-time processing of algorithms. This paper proposes a real-time pose measurement method based on binocular vision that is suitable for low-light environments. Firstly, the traditional point feature extraction algorithm is adaptively improved based on lighting conditions, greatly reducing the impact of lighting on the effectiveness of feature point extraction. By combining point feature matching with epipolar constraints, the matching range of feature points is narrowed down to the epipolar line, significantly improving the matching speed and accuracy. Secondly, utilizing the structural information of the spacecraft, line features are introduced and processed in parallel with point features, greatly enhancing the accuracy of pose measurement results. Finally, an adaptive weighted multi-feature pose fusion method based on lighting conditions is introduced to obtain the optimal pose estimation results. Simulation and physical experiment results demonstrate that this method can obtain high-precision target pose information in a real-time and stable manner, both in well-lit and low-light environments. Full article

Surface-related multiple suppression is a critical step in seismic data processing, while traditional adaptive matching subtraction methods often distort primaries, resulting in either the leakage of primaries or the residue of surface-related multiples. To address these challenges, we propose a field-parameter-guided semi-supervised learning (FPSSL) method to more effectively eliminate surface-related multiples. Field parameters refer to the time–space coordinate information derived from the seismic acquisition system, including offsets, trace spaces, and sampling intervals. These parameters reveal the relative positional relationships of seismic data in the time–space domain. The FPSSL framework comprises a supervised network module (SNM) and an unsupervised network module (USNM). The input and output data of the SNM are a small sample of full wavefield data and the weights of a polynomial function, respectively. A linear weighted sum method is employed to represent the SNM outputs (weights), the full wavefield data, and field parameters as a polynomial function of the primaries, which is matched with adaptive subtraction label data. The trained SNM generates preliminary estimates of the primaries and multiples with improved lateral continuity from full wavefield data, both of which are used as inputs to the USNM. The USNM is essentially an optimization operator that refines the underlying nonlinear mapping relationship between primaries and full wavefield data using the local wavefield feature loss function, thereby obtaining more accurate prediction results with respect to primaries. Examples from synthetic data and real marine data demonstrate that the FPSSL method surpasses the traditional L1-norm adaptive subtraction method in suppressing multiples, significantly reducing the leakage of primaries and the residuals of surface-related multiples in the estimated demultiple results. The effectiveness and efficiency of our proposed method are verified through two sets of synthetic data and one marine data example. Full article

Three-dimensional reconstruction is an essential skill for robots to achieve complex operation tasks, including moving and grasping. Applying deep learning models to obtain stereoscopic scene information, accompanied by algorithms such as target detection and semantic segmentation to obtain finer labels of things, is the dominant paradigm for robots. However, large-scale point cloud registration and pixel-level labeling are usually time-consuming. Here, a novel two-branch network architecture based on PointNet features is designed. Its feature-sharing mechanism enables point cloud registration and semantic extraction to be carried out simultaneously, which is convenient for fast reconstruction of indoor environments. Moreover, it uses graph space instead of Euclidean space to map point cloud features to obtain better relationship matching. Through extensive experimentation, our method demonstrates a significant reduction in processing time, taking approximately one-tenth of the time required by the original method without a decline in accuracy. This efficiency enhancement enables the successful execution of downstream tasks such as positioning and navigation. Full article

Coordinate registration (CR) is the key technology for improving the target positioning accuracy of sky-wave over-the-horizon radar (OTHR). The CR parameters are derived by matching the sea–land clutter classification (SLCC) results with prior geographic information. However, the SLCC results often contain mixed clutter, leading to discrepancies between land and island contours and prior geographic information, which makes it challenging to calculate accurate CR parameters for OTHR. To address these challenges, we transform the sea–land clutter data from Euclidean space into graph data in non-Euclidean space, and the CR parameters are obtained by calculating the similarity between graph pairs. And then, we propose a similarity calculation via a graph neural network (SC-GNN) method for calculating the similarity between graph pairs, which involves subgraph-level interactions and node-level comparisons. By partitioning the graph into subgraphs, SC-GNN effectively captures the local features within the SLCC results, enhancing the model’s flexibility and improving its performance. For validation, we construct three datasets: an original sea–land clutter dataset, a sea–land clutter cluster dataset, and a sea–land clutter registration dataset, with the samples drawn from various seasons, times, and detection areas. Compared with the existing graph matching methods, the proposed SC-GNN achieves a Spearman’s rank correlation coefficient of at least 0.800, a Kendall’s rank correlation coefficient of at least 0.639, a p@10 of at least 0.706, and a p@20 of at least 0.845. Full article

The lunar south pole has gained significant attention due to its unique scientific value and potential for supporting future human exploration. Its potential water ice reservoirs and favourable conditions for long-term habitation make it a strategic target for upcoming space missions. This has led to a continuous increase in missions towards the Moon thanks mainly to the boost provided by NASA’s Artemis programme. This study focuses on designing a satellite constellation to provide communication coverage for the lunar south pole. Among the various cislunar orbits analysed, the halo orbit families near Earth–Moon Lagrangian points L1 and L2 emerged as the most suitable ones for ensuring continuous communication while minimising the number of satellites required. These orbits, first described by Farquhar in 1966, allow spacecraft to maintain constant communication with Earth due to their unique geometric properties. The candidate orbits were initially implemented in MATLAB using the Circular Restricted Three-Body Problem (CR3BP) to analyse their main features such as stability, periodicity, and coverage time percentage. In order to develop a more detailed and realistic scenario, the obtained initial conditions were refined using a full ephemeris model, incorporating a ground station located near the Connecting Ridge Extension to evaluate communication performance depending on the minimum elevation angle of the antenna. Different multi-body constellations were propagated; however, the constellation consisting of three satellites around L2 and a single satellite around L1 turned out to be the one that best matches the coverage requirements. Full article

As the global demand for surveillance cameras increases, the digital footage data also explicitly increases. Analyzing and extracting meaningful content from footage is a resource-depleting and laborious effort. The traditional video synopsis technique is used for constructing a small video by relocating the object in the time and space domains. However, it is computationally expensive, and the obtained synopsis suffers from jitter artifacts; thus, it cannot be hosted on a resource-constrained device. In this research, we propose a panoramic video synopsis framework to address and solve the problems of the efficient analysis of objects for better governance and storage. The surveillance system has multiple cameras sharing a common homography, which the proposed method leverages. The proposed method constructs a panorama by solving the broad viewpoints with significant deviations, collisions, and overlapping among the images. We embed a synopsis framework on the end device to reduce storage, networking, and computational costs. A neural network-based model stitches multiple camera feeds to obtain a panoramic structure from which only tubes with abnormal behavior were extracted and relocated in the space and time domains to construct a shorter video. Comparatively, the proposed model achieved a superior accuracy matching rate of 98.7% when stitching the images. The feature enhancement model also achieves better peak signal-to-noise ratio values, facilitating smooth synopsis construction. Full article

In image-guided surgery (IGS) practice, combining intraoperative 2D X-ray images with preoperative 3D X-ray images from computed tomography (CT) enables the rapid and accurate localization of lesions, which allows for a more minimally invasive and efficient surgery, and also reduces the risk of secondary injuries to nerves and vessels. Conventional optimization-based methods for 2D X-ray and 3D CT matching are limited in speed and precision due to non-convex optimization spaces and a constrained searching range. Recently, deep learning (DL) approaches have demonstrated remarkable proficiency in solving complex nonlinear 2D–3D registration. In this paper, a fast and robust DL-based registration method is proposed that takes an intraoperative 2D X-ray image as input, compares it with the preoperative 3D CT, and outputs their relative pose in x, y, z and pitch, yaw, roll. The method employs a dual-channel Swin transformer feature extractor equipped with attention mechanisms and feature pyramid to facilitate the correlation between features of the 2D X-ray and anatomical pose of CT. Tests on three different regions of interest acquired from open-source datasets show that our method can achieve high pose estimation accuracy (mean rotation and translation error of 0.142° and 0.362 mm, respectively) in a short time (0.02 s). Robustness tests indicate that our proposed method can maintain zero registration failures across varying levels of noise. This generalizable learning-based 2D (X-ray) and 3D (CT) registration algorithm owns promising applications in surgical navigation, targeted radiotherapy, and other clinical operations, with substantial potential for enhancing the accuracy and efficiency of image-guided surgery. Full article

With the rapid advancement of aerospace technology, the maneuverability of spacecraft has increasingly improved, creating a pressing demand for star sensors with a high attitude update rate and high precision. Star identification, as the most complex and time-consuming algorithm of star sensors, faces stringent requirements for enhanced identification speed and an enhanced identification rate. Furthermore, as the space environment is becoming more complex, the need for star sensors with heightened detection sensitivity is growing to facilitate real-time and accurate alerts for various non-cooperative targets, which has led to a sharp increase in the number of high-magnitude navigation stars in the star catalog, significantly impeding the speed and rate of star identification. Traditional methods are no longer adequate to meet the current demand for star sensors with high identification speed and a high identification rate. Addressing these challenges, a voting-based star identification algorithm using a partitioned star catalog is proposed. Initially, a uniform partitioning method for the star catalog is introduced. Building on this, a navigation feature library using partitioned catalog neighborhoods as a basic unit is constructed. During star identification, a method based on a voting decision is employed for feature matching in the basic unit. Compared to conventional methods, the proposed algorithm significantly simplifies the navigation feature library and narrows the retrieval region during star identification, markedly enhancing identification speed while effectively reducing the probability of redundant and false matching. The performance of the proposed algorithm is validated through a simulation experiment and nighttime star observation experiment. Experimental results indicate an average identification rate of 99.760% and an average identification time of 8.861 milliseconds, exhibiting high robustness against position errors, magnitude errors, and false stars. The proposed algorithm presents a clear advantage over other common star identification methods, meeting the current requirement for star sensors with high star identification speed and a high identification rate. Full article

The effective management and conservation of forest resources hinge on accurate monitoring. Nonetheless, individual remote-sensing images captured by low-altitude unmanned aerial vehicles (UAVs) fail to encapsulate the entirety of a forest’s characteristics. The application of image-stitching technology to high-resolution drone imagery facilitates a prompt evaluation of forest resources, encompassing quantity, quality, and spatial distribution. This study introduces an improved SIFT algorithm designed to tackle the challenges of low matching rates and prolonged registration times encountered with forest images characterized by dense textures. By implementing the SIFT-OCT (SIFT omitting the initial scale space) approach, the algorithm bypasses the initial scale space, thereby reducing the number of ineffective feature points and augmenting processing efficiency. To bolster the SIFT algorithm’s resilience against rotation and illumination variations, and to furnish supplementary information for registration even when fewer valid feature points are available, a gradient location and orientation histogram (GLOH) descriptor is integrated. For feature matching, the more computationally efficient Manhattan distance is utilized to filter feature points, which further optimizes efficiency. The fast sample consensus (FSC) algorithm is then applied to remove mismatched point pairs, thus refining registration accuracy. This research also investigates the influence of vegetation coverage and image overlap rates on the algorithm’s efficacy, using five sets of Cyclobalanopsis natural forest images. Experimental outcomes reveal that the proposed method significantly reduces registration time by an average of 3.66 times compared to that of SIFT, 1.71 times compared to that of SIFT-OCT, 5.67 times compared to that of PSO-SIFT, and 3.42 times compared to that of KAZE, demonstrating its superior performance. Full article

The paper presents research related to coastal observation using a camera and LiDAR (Light Detection and Ranging) mounted on an unmanned surface vehicle (USV). Fusion of data from these two sensors can provide wider and more accurate information about shore features, utilizing the synergy effect and combining the advantages of both systems. Fusion is used in autonomous cars and robots, despite many challenges related to spatiotemporal alignment or sensor calibration. Measurements from various sensors with different timestamps have to be aligned, and the measurement systems need to be calibrated to avoid errors related to offsets. When using data from unstable, moving platforms, such as surface vehicles, it is more difficult to match sensors in time and space, and thus, data acquired from different devices will be subject to some misalignment. In this article, we try to overcome these problems by proposing the use of a point matching algorithm for coordinate transformation for data from both systems. The essence of the paper is to verify algorithms based on selected basic neural networks, namely the multilayer perceptron (MLP), the radial basis function network (RBF), and the general regression neural network (GRNN) for the alignment process. They are tested with real recorded data from the USV and verified against numerical methods commonly used for coordinate transformation. The results show that the proposed approach can be an effective solution as an alternative to numerical calculations, due to process improvement. The image data can provide information for identifying characteristic objects, and the obtained accuracies for platform dynamics in the water environment are satisfactory (root mean square error—RMSE—smaller than 1 m in many cases). The networks provided outstanding results for the training set; however, they did not perform as well as expected, in terms of the generalization capability of the model. This leads to the conclusion that processing algorithms cannot overcome the limitations of matching point accuracy. Further research will extend the approach to include information on the position and direction of the vessel. Full article