Search Results (582)

Point cloud processing of raw scan data is a critical step to enhance the accuracy and efficiency in computer-aided inspection and remanufacturing of damaged aero-engine blades. This paper presents a new methodology to obtain a noise-reduced and simplified dataset from the raw scan data while preserving the underlying geometry of the damaged blade in high-curvature and damaged regions. At first, outliers are removed from the scan data, and measurement noise is reduced through local least-squares quadric surface/plane fitting on the adaptive support domain of measured points under the measurement uncertainty constraint of inspection data. Then, a directed Hausdorff distance-based region growing scheme is developed to progressively search within the support domain of denoised data points to obtain a down-sampled dataset while preserving the local geometric shape of the surface. Numerical and experimental case studies have been conducted to evaluate the accuracy and computation time of scan-to-CAD rigid registration and CAD-to-scan non-rigid registration processes using the down-sampled dataset of damaged blades. The results have demonstrated that the proposed methodology effectively removes the measurement noise and outliers and provides a down-sampled dataset from the scan data that can significantly reduce the time complexity of the computer-aided inspection and remanufacturing process of the point cloud of damaged blades with a negligible loss of accuracy. Full article

The accurate positioning and state estimation of surface vessels are prerequisites to autonomous navigation. Recently, the rapid development of 3D LiDARs has promoted the autonomy of both land and aerial vehicles, which has attracted the interest of researchers in the maritime community. However, in traditional maritime surface multi-scenario applications, LiDAR scan matching has low point cloud scanning and matching efficiency and insufficient positional accuracy when dealing with large-scale point clouds, so it has difficulty meeting the real-time demand of low-computing-power platforms. In this paper, we use ICP-SVD for point cloud alignment in the Stanford dataset and outdoor dock scenarios and propose an optimization scheme (iVox + ICP-SVD) that incorporates the voxel structure iVox. Experiments show that the average search time of iVox is 72.23% and 96.8% higher than that of ikd-tree and kd-tree, respectively. Executed on an NVIDIA Jetson Nano (four ARM Cortex-A57 cores @ 1.43 GHz) the algorithm processes 18 k downsampled points in 56 ms on average and 65 ms in the worst case—i.e., ≤15 Hz—so every scan is completed before the next 10–20 Hz LiDAR sweep arrives. During a 73 min continuous harbor trial the CPU temperature stabilized at 68 °C without thermal throttling, confirming that the reported latency is a sustainable, field-proven upper bound rather than a laboratory best case. This dramatically improves the retrieval efficiency while effectively maintaining the matching accuracy. As a result, the overall alignment process is significantly accelerated, providing an efficient and reliable solution for real-time point cloud processing. Full article

Automated crop monitoring in controlled environments is imperative for enhancing crop productivity. The availability of small unmanned aerial systems (sUAS) and cost-effective LiDAR sensors present an opportunity to conveniently gather high-quality data for crop monitoring. The LiDAR-collected point cloud data, however, often encounter challenges such as occlusions and low point density that can be addressed by acquiring additional data from multiple flight paths. This study evaluated the performance of using an Iterative Closest Point (ICP)-based algorithm for registering sUAS-based LiDAR point clouds collected in the greenhouse environment. To address the issue of objects that may cause ICP or local feature-based registration to mismatch correspondences, this study developed a robust registration pipeline. First, the geometric centroid of the ground floor boundary was leveraged to improve the initial alignment, and then piecewise ICP was implemented to achieve fine registration. The evaluation of point cloud registration performance included visualization, root mean square error (RMSE), volume estimation of reference objects, and the distribution of point cloud density. The best RMSE dropped from 20.4 cm to 2.4 cm, and point cloud density improved after registration, and the volume-estimation error for reference objects dropped from 72% (single view) to 6% (post-registration). This study presents a promising approach to point cloud registration that outperforms conventional ICP in greenhouse layouts while eliminating the need for artificial reference objects. Full article

In aluminum electrolysis workshops, real-time pose perception of shelling heads is crucial to process accuracy and equipment safety. However, due to high temperatures, smoke, dust, and metal obstructions, traditional pose estimation methods struggle to achieve high accuracy and robustness. At the same time, the continuous movement of the shelling head and the similar geometric structures around it make it hard to match point-clouds, which makes it even harder to track the position and orientation. In response to the above challenges, we propose a multi-stage optimization pose estimation algorithm based on point-cloud processing. This method is designed for dynamic perception tasks of three-dimensional components in complex industrial scenarios. First stage improves the accuracy of initial matching by combining a weighted 3D Hough voting and adaptive threshold mechanism with an improved FPFH feature matching strategy. In the second stage, by integrating FPFH and PCA feature information, a stable initial registration is achieved using the RANSAC-IA coarse registration framework. In the third stage, we designed an improved ICP algorithm that effectively improved the convergence of the registration process and the accuracy of the final pose estimation. The experimental results show that the proposed method has good robustness and adaptability in a real electrolysis workshop environment, and can achieve pose estimation of the shelling head in the presence of noise, occlusion, and complex background interference. Full article

To detect damage in underwater pile foundations, we propose a new method based on the diffusion probability model and improved PointMLP. First, PCA-ICP registration is carried out for the point cloud data from different stations using a sonar system. A variety of filtering algorithms and the Random Sample Consensus (RANSAC) method are employed to obtain a complete point cloud of the pile foundation. The pile foundation defect point cloud is generated and enhanced based on the diffusion probability model. The feature attention mechanism is added to the PointMLP, and then the improved PointMLP is trained to identify the defect of the pile foundation. In our study, the point cloud of a wharf pile foundation was collected, and the experimental results effectively identified the damage to the pile foundation. Up to 95% accuracy was achieved for the calculated volume. The volume error of the damage was 0.0756 m³, with an accuracy of 95.238%. Thus, this method can provide technical support for detecting underwater pile foundation defects and avoiding the occurrence of major accidents. Full article

Simultaneous Localization and Mapping (SLAM) systems require accurate and globally consistent mapping to ensure the long-term stable operation of robots or vehicles. However, for the commercial applications of indoor sweeping robots, the system needs to maintain accuracy while keeping computational and storage requirements low to ensure cost controllability. This paper proposes a dual-point-cloud-registration SLAM based on line features for the mapping of a mobile robot, named DPCR-SLAM. The front-end employs an improved Point-to-Line Iterative Closest Point (PLICP) algorithm for point cloud registration. It first aligns the point cloud and updates the submap. Subsequently, the submap is aligned with the regional map, which is then updated accordingly. The back-end uses the association between regional maps to perform graph optimization and update the global map. The experimental results show that, in the application scenario of indoor sweeping robots, the proposed method reduces the map storage space by 76.3%, the point cloud processing time by 55.8%, the graph optimization time by 77.7%, and the average localization error by 10.9% compared to the Cartographer, which is commonly used in the industry. Full article

In the field of Autonomous Surface Vehicle (ASV), research on advanced perception technologies is crucial for enhancing their intelligence and autonomy. In particular, laser point cloud registration technology serves as a foundation for improving the navigation accuracy and environmental awareness of ASV in complex environments. To address the issues of low computational efficiency, insufficient robustness, and incompatibility with low-power devices in laser point cloud registration technology for ASV, a novel point cloud matching method has been proposed. The proposed method includes laser point cloud data processing, feature extraction based on an improved Fast Point Feature Histogram (FPFH), followed by a two-step registration process using SAC-IA (Sample Consensus Initial Alignment) and Small_GICP (Small Generalized Iterative Closest Point). Registration experiments conducted on the KITTI benchmark dataset and the Pohang Canal dataset demonstrate that the relative translation error (RTE) of the proposed method is 16.41 cm, which is comparable to the performance of current state-of-the-art point cloud registration algorithms. Furthermore, deployment experiments on multiple low-power computing devices showcase the performance of the proposed method under low computational capabilities, providing reference metrics for engineering applications in the field of autonomous navigation and perception research for ASV. Full article

This paper introduces a novel Underclothing Body Shape Perception Network (UBSP-Net) for reconstructing parametric 3D human models from clothed full-body 3D scans, addressing the challenge of estimating body shape and pose beneath clothing. Our approach simultaneously predicts both the internal body point cloud and a reference point cloud for the SMPL model, with point-to-point correspondence, leveraging the external scan as an initial approximation to enhance the model’s stability and computational efficiency. By learning point offsets and incorporating body part label probabilities, the network achieves accurate internal body shape inference, enabling reliable Skinned Multi-Person Linear (SMPL) human body model registration. Furthermore, we optimize the SMPL+D human model parameters to reconstruct the clothed human model, accommodating common clothing types, such as T-shirts, shirts, and pants. Evaluated on the CAPE dataset, our method outperforms mainstream approaches, achieving significantly lower Chamfer distance errors and faster inference times. The proposed automated pipeline ensures accurate and efficient reconstruction, even with sparse or incomplete scans, and demonstrates robustness on real-world Thuman2.0 dataset scans. This work advances parametric human modeling by providing a scalable and privacy-preserving solution for applications to 3D shape analysis, virtual try-ons, and animation. Full article

To address the problem of point-cloud registration accuracy degradation or even failure in traditional Voxelized GICP(VGICP) under bad initial pose due to improper voxel resolution settings, this paper proposes an Adaptive Resolution VGICP (AR-VGICP) algorithm. The algorithm first automatically estimates the initial voxel resolution based on the absolute deviations between source points outside the target voxel grid and their nearest neighbors in the target cloud, using the Median Absolute Deviation (MAD) method, and performs initial registration. Subsequently, the voxel resolution is dynamically updated according to the average nearest neighbor distance between the transformed source points and the target points, enabling progressive refined registration. The resolution update process terminates until the resolution change rate falls below a predefined threshold or the updated resolution does not exceed the density-adaptive resolution. Experimental results on both simulated and real-world datasets demonstrate that AR-VGICP achieves a 100% registration success rate, while VGICP fails in some cases due to small voxel resolution. On the KITTI dataset, AR-VGICP reduces translation error by 9.4% and rotation error by 14.8% compared to VGICP with a fixed 1 m voxel resolution, while increasing computation time by only 3%. Results from UAV LiDAR experiments show that, in residential area data, AR-VGICP achieves a maximum reduction of 33.4% in translation error and 21.4% in rotation error compared to VGICP (1.0 m). These results demonstrate that AR-VGICP attains a higher registration success rate when the initial pose between point-cloud pairs is bad, and delivers superior point-cloud registration accuracy in urban scenarios compared to VGICP. Full article

This study proposes an Automatic Branch Modeling (ABM) framework that combines AdTree and AdQSM algorithms to reconstruct individual tree models and estimate timber volume from fused Hand-held Laser Scanners (HLS) and Unmanned Aerial Vehicle Laser Scanners (UAV-LS) point cloud data. The research focuses on two 50 × 50 m primary tropical rainforest plots in Hainan Island, China, characterized by dense and vertically stratified vegetation. Key steps include multi-source point cloud registration and noise removal, individual tree segmentation using the Comparative Shortest Path (CSP) algorithm, extraction of diameter at breast height (DBH) and tree height, and 3D reconstruction and volume estimation via cylindrical fitting and convex polyhedron decomposition. Results demonstrate high accuracy in parameter extraction, with DBH estimation achieving R² = 0.89–0.90, RMSE = 2.93–3.95 cm and RMSE% = 13.95–14.75%, while tree height estimation yielded R² = 0.89–0.94, RMSE = 1.26–1.81 m and RMSE% = 9.41–13.2%. Timber volume estimates showed strong agreement with binary volume models (R² = 0.90–0.94, RMSE = 0.10–0.18 m³, RMSE% = 32.33–34.65%), validated by concordance correlation coefficients (CCC) of 0.95–0.97. The fusion of HLS (ground-level trunk details) and UAV-LS (canopy structure) data significantly improved structural completeness, overcoming occlusion challenges in dense forests. This study highlights the efficacy of multi-source LiDAR fusion and 3D modeling for precise forest inventory in complex ecosystems. The ABM framework provides a scalable, non-destructive alternative to traditional methods, supporting carbon stock assessment and sustainable forest management in tropical rainforests. Future work should refine individual tree segmentation and wood-leaf separation to further enhance accuracy in heterogeneous environments. Full article

This paper introduces an innovative automatic trajectory generation method for the robotic deburring of molded parts, effectively addressing challenges posed by burr defects and workpiece deformation common in casting and injection molding processes. Existing offline trajectory planning methods often struggle with substantial burr sizes and complex surface deformations, resulting in compromised machining quality due to over-adaptation. To overcome these issues, the proposed approach utilizes 3D vision techniques to achieve precise burr localization. A novel burr point cloud segmentation method based on feature analysis, combined with a tolerance-constrained non-rigid registration algorithm, accurately identifies burr regions and optimizes trajectory positioning within defined manufacturing tolerances. Furthermore, the method employs quantitative burr height distribution analysis to dynamically adjust robotic feed rates, significantly enhancing processing efficiency. Experimental validations demonstrated that the proposed method reduces the deburring time by up to 68% compared to conventional techniques, achieving an average trajectory deviation of only 0.79 mm. This study provides a robust, efficient, and precise solution for automating deburring operations in complex molded components, highlighting its substantial potential for industrial applications. Full article

Multi-source topographic point clouds are of great value in applications such as mine monitoring, geological hazard assessment, and high-precision terrain modeling. However, challenges such as heterogeneous data sources, drastic terrain variations, and significant differences in point density severely hinder accurate registration. To address these issues, this study proposes a robust point cloud registration method named Cauchy-AdaV2, which integrates region-adaptive weighting with Cauchy-based residual suppression. The method jointly leverages slope and roughness to partition terrain into regions and constructs a spatially heterogeneous weighting function. Meanwhile, the Cauchy M-estimator is employed to mitigate the impact of outlier correspondences, enhancing registration accuracy while maintaining adequate correspondence coverage. The results indicate that the proposed method significantly outperforms traditional ICP, GICP, and NDT methods in terms of overall error metrics (MAE, RMSE), error control in complex terrain regions, and cross-sectional structural alignment. Specifically, it achieves a mean absolute error (MAE) of 0.0646 m and a root mean square error (RMSE) of 0.0688 m, which are 70.5% and 72.4% lower than those of ICP, respectively. These outcomes demonstrate that the proposed method possesses stronger spatial consistency and terrain adaptability. Ablation studies confirm the complementary benefits of regional and residual weighting, while efficiency analysis shows the method to be practically applicable in large-scale point cloud scenarios. This work provides an effective solution for high-precision registration of heterogeneous point clouds, especially in challenging environments characterized by complex terrain and strong disturbances. Full article

With the rapid advancement in laser scanning technologies, the capability to collect massive volumes of data and richer detailed features has been significantly enhanced. However, the differential representation ability of multi-source point clouds in capturing intricate structures within complex scenes, combined with the computational burden imposed by large datasets, presents substantial challenges to current registration methods. The proposed method encompasses two innovative feature point pruning techniques and two closely interconnected progressive processes. First, it identifies structural points that effectively represent the features of the scene and performs a rapid initial alignment of point clouds within the two-dimensional plane. Subsequently, it establishes the mapping relationship between the point clouds to be matched utilizing FPFH descriptors, followed by further screening to extract the maximum consensus set composed of points that meet constraints based on the intensity of graph nodes. Then, it integrates the processes of feature point description and similarity measurement to achieve precise point cloud registration. The proposed method effectively extracts matching primitives from large datasets, addressing issues related to false matches and noise in complex data environments. It has demonstrated favorable matching results even in scenarios with low overlap between datasets. On two public datasets and a self-constructed dataset, the method achieves an effective point set screening rate of approximately 1‰. On the WHU-TLS dataset, our method achieves a registration accuracy characterized by a rotation precision of 0.062° and a translation precision of 0.027 m, representing improvements of 70% and 80%, respectively, over current state-of-the-art (SOTA) methods. The results obtained from real registration tasks demonstrate that our approach attains competitive registration accuracy when compared with existing SOTA techniques. Full article

Scanned data often contain substantial outliers due to environmental interference, which drastically decreases the performance of traditional registration algorithms. To address this issue, this article proposes an improved principal component analysis (PCA) and Jacobian-enhanced whale optimization collaborative method for point cloud registration. First, an improved PCA point cloud initial registration algorithm is proposed by introducing the normal vector local information to set the screening conditions. This algorithm can streamline the original set of 48 candidate rotation matrices down to 4, achieving rapid point cloud registration at the data level between the scanned and model point clouds. Second, a Jacobian whale optimization algorithm for fine registration (JWOA-FR) is proposed by incorporating local gradient information. The algorithm employs gradient descent on optimal whale individuals to dynamically guide global search updates, thereby enhancing both registration accuracy and efficiency. Finally, a threshold is set to remove the outliers contained in the workpieces based on the information of the matched point pairs. The iterative closest point (ICP) algorithm is further used to improve registration accuracy for data without outliers. The experimental results showed that registration errors of large workpieces 1, 2, and 3 were 2.0755 mm, 2.3955 mm, and 2.5823 mm, respectively, after outlier removal, which indicates that the proposed method is applicable to data with outliers, and the registration accuracy meets the requirements. Full article

Accurate registration of multi-platform light detection and ranging (LiDAR) point clouds is essential for detailed forest structure analysis and ecological monitoring. In this study, we developed a novel two-stage method for aligning terrestrial and unmanned aerial vehicle LiDAR point clouds in forest environments. The method first performs coarse alignment using canopy-level digital surface models and Fast Point Feature Histograms, followed by fine registration with Iterative Closest Point. Experiments conducted in six forest plots achieved an average registration accuracy of 0.24 m within 5.14 s, comparable to manual registration but with substantially reduced processing time and human intervention. In contrast to existing tree-based methods, the proposed approach eliminates the need for individual tree segmentation and ground filtering, streamlining preprocessing and improving scalability for large-scale forest monitoring. The proposed method facilitates a range of forest applications, including structure modeling, ecological parameter retrieval, and long-term change detection across diverse forest types and platforms. Full article