As manufacturing systems become increasingly automated and require broader operational spaces, robotic vision and industrial inspection systems are encountering new challenges in larger and more complex environments. In this context, high-precision spot center localization has become increasingly critical for large-scale measurement scenarios. However, under large-scale working conditions, conventional localization algorithms often produce subpixel errors due to spot degradation, reduced signal-to-noise ratios, and elliptical distortion, which can lead to downstream processing deviations or detection failures. To address this, we propose a novel feature selection and localization framework that combines planar-constrained radial gradient fields with a Gaussian surface fitting algorithm. The approach first enhances robustness by extracting and refining candidate regions through gradient consistency and intensity distribution analysis, then achieves sub-pixel accuracy by integrating inverse Gaussian CDF modeling with a probabilistic weighting mechanism for nonlinear surface fitting. The approach significantly improves localization accuracy and robustness under dynamic distances, low-signal conditions, and elliptical distortion, overcoming performance bottlenecks in existing methods during scale expansion. Applied to a spatial optical stylus measurement system, the proposed method achieves high accuracy, with an average measurement error of only 0.0695 mm over a distance range of 4 to 10 m. The algorithm demonstrates both strong measurement accuracy and robustness, indicating its potential for reliable deployment in real-world engineering scenarios.

Complex free-form surface machining plays a critical role in biomedical and customized manufacturing, where high manufacturing accuracy is essential for ensuring functional and anatomical performance. Traditional iso-scallop tool path planning methods control scallop height to enhance surface quality. However, these methods often face challenges in balancing struggle to balance machining efficiency with and precision, leading to resulting in redundant tool paths or uncut regions, particularly when applied to triangular mesh models. This study addresses these limitations by introducing To address these limitations, this study introduces a geodesic-based framework that constructs curvature-adaptive contours as tool paths using the heat flow method. A novel strategy based on direction field singularities and a Minimal Envelope Line is developed to accurately detect uncut regions, which are subsequently resolved through globally optimized Clean-Paths. Numerical experiments on both synthetic and anatomical surfaces confirm the effectiveness of the proposed method. In the Molar Crown case, the method achieves a 68.37

The camera imaging model reflects the mapping relationship between three-dimensional spatial coordinates and two-dimensional image plane coordinates, serving as a key factor in assessing the geometric accuracy of camera imaging. In practical engineering applications, optical distortion causes camera imaging to deviate from the pinhole imaging principle, necessitating correction to improve geometric accuracy. The scale of the stationary orbit large-area array camera detector has significantly increased, and traditional distortion correction methods are limited by the high-precision angle measurement range of the two-dimensional turntable, making it impossible to achieve high-precision sampling of the global range measurement points of the array detector; thus, the distortion correction accuracy cannot be guaranteed. This paper proposes an extrapolation distortion correction method based on the measurement data of the sub-area of the area array detector, taking into account the practical situation of the area array camera engineering and the good extrapolation characteristics of the polynomial model. The experimental results show that the method proposed in this paper can effectively achieve distortion correction of area array cameras, and the average and standard deviation of the correction results reach the sub-pixel level, which are 0.4930 pixels and 0.1833 pixels, respectively. Compared with traditional distortion correction methods, the correction accuracy of our proposed method is significantly better than that of traditional models. The average and standard deviation of the correction results are improved by 40.22% and 71.91%, respectively.

Accurate measurement of parts with high-dynamic-range (HDR) surfaces remains challenging in industrial scenes. Recently, four main strategies have been developed for effective HDR surface measurement, such as multi-exposure fusion, projection intensity modulation, hardware-assisted methods, and multi-view stereo (MVS). However, these methods cannot simultaneously provide single-shot, fast and precise measurement. Current learning-based measurement methods overlook restricted receptive fields and coarse initialization on HDR surfaces, reducing measurement precision. In this paper, we propose a learning-based measurement method for HDR surfaces using speckle projection profilometry with a conditional diffusion MVS model to address these limitations. Differing from prior methods, depth refinement is modeled with conditional diffusion and combined with multi-view stereo to improve HDR surface measurement robustness. To enhance feature representation on HDR surfaces, a multi-scale feature extraction module with deformable convolution is employed for adaptive receptive field adjustment. A conditional diffusion model is developed for integrating matching information, image context, and depth cues to iteratively denoise and progressively recover more accurate depth maps while mitigating local minima. Finally, a depth map fusion module combines the depth estimates to output a high-precision point cloud. Quantitative experiments on HDR datasets and real workpieces demonstrate that the method achieves accuracy of 0.03-0.06 mm, outperforming state-of-the-art learning-based approaches. The inference time is 0.353 s with 7.80 GB GPU usage, indicating its potential for lightweight deployment.

Multiple refractions between different media lead to camera refractive distortions, thereby making it difficult to ensure underwater measurement accuracy. Consequently, precise modeling and calibration of underwater multilayer imaging systems is essential to address these distortions. In this paper, employing a perspective projection scale factor along the light propagation direction, a universal forward multilayer refractive model is presented. On this basis, the refractive model incorporates a general rotation matrix, which not only enriches application diversity but also enhances solution precision. Allowing for direct underwater camera calibration without prior calibration in air, the model simplifies the calibration process and enhances its suitability for practical applications. Through underwater calibration experiments, the refractive model enhances solution efficiency, improves identification precision and distributes evenly and mediately in terms of reprojection error. In addition, it increases the number of identifiable parameters, demonstrating its robustness and superiority in complex parameter identification scenarios. Finally, underwater measurement experiments indicate that the absolute accuracy calibrated and measured by the refractive model is better than 0.25 mm. Overall, combining all the benefits of forward and backward compensation methods, the forward refractive model offers versatility in handling multiple refractions without limited refractive layers.

The optical 3D shape measurement method with digital speckle pattern projection is widely applied in dynamic measurement and industrial applications. When the surface to be measured is complex, existing speckle projection profilometry (SPP) methods face challenges such as low accuracy and efficiency due to the use of a fixed window size, occlusion, and high computational cost. Although existing multi-view reconstruction algorithms can address occlusion issues and enhance measurement robustness, they are not applicable to industrial fields. Additionally, existing stereo matching algorithms also have several limitations in terms of window size adaptability and efficiency. In this paper, a multi-view measurement method by single shot with curvature-aware adaptive window size selection is proposed. A multi-view stereo matching pipeline based on photometric and geometric consistency is proposed for initial reconstruction, with depth map refinement using the adaptive window. A sequential circular sampling strategy is proposed to obtain high-consistency neighborhood point clouds for curvature-aware scoring. Subsequently, curvature-aware composite scoring is performed based on plane fitting error, normal vector angle, and matching cost difference to enable adaptive window size selection. After iterating for multi-view consistency under the adaptive window, multiple depth maps are fused to generate the final point cloud. A four-camera measurement system, has been developed for experimental validation. Extensive experiments show that the proposed method achieves a surface measurement accuracy of approximately 0.04 mm, reducing the standard deviation by 9.9% to 26.5% compared to state-of-the-art methods. It maintains comparable runtime and computational complexity, and meets the industrial application requirements.

An image motion compensation method is proposed for the SDLT-1 satellite of China and an experiment based on the three-axis air-bearing physical simulator equipment is designed to testify the feasibility of the proposed method. The experiment shows that the deviation of the LOS after AMC compensation can reach 10.96urad ( 3σ ) and 97 3σ ).

Due to the limited dynamic range of cameras, reconstruction tasks on high dynamic range surfaces often exhibit overexposed and underexposed regions in the captured images, compromising measurement accuracy and completeness. Existing high dynamic range measurement techniques perform 3D reconstruction directly using image intensity or multi-exposure fusion methods. These techniques cannot simultaneously meet the requirements of single-shot measurement, high precision, high completeness, and ease of use. In this article, we propose a novel measurement method with single shot to address these challenges. Different from existing methods, a multi-view stereo pipeline is proposed to fully utilize the properly exposed areas of different views. By multi-view matching based on photometric and geometric consistency, the depth and normal vector for each pixel in every view will be obtained. The depth and normal vector can be combined into a spatial slanted plane, initializing the first-order shape function parameters. A multi-view digital speckle correlation method is proposed to optimize the plane parameters. Finally, the plane parameters are integrated to produce the depth map fusion. A four-camera measurement system with a photolithographic speckle projection module is developed for experimental validation. Extensive quantitative and qualitative experiments demonstrate that the proposed method achieves the measurement accuracy of 0.04-0.07 mm, meeting the requirements of industrial applications.

As a huge heat source, the fuel assembly directly causes its surrounding dynamic temperature field and subsequently induces the irregular real-time variations of refractive index. Consequently, thermal turbulence phenomena inevitably occur, affecting the measurement accuracy when measuring the bow and twist deformation of fuel assembly. To mitigate the impact brought by the thermal turbulence, an improved U-Net network that boasts exceptional restoration efficiency and precision is presented. Utilizing a linear activation function, corresponding experimental datasets are captured and preprocessed for further training. The principle of thermal turbulence phenomenon is elaborated and the corresponding existence is demonstrated by field turbulence experiments. Compared to thermal turbulence conditions, field restoration experiments demonstrate that our restoration model significantly reduces the impact of image fluctuations and temporal fluctuations, prominently enhances the accuracy of center coordinate extraction, and subsequently provides a strong guarantee for the field deformation measurement of fuel assembly.

Accurate positioning of surface geometric features is essential for quality inspection of automotive components. While vision-based robotic measurement systems are extensively employed, residual errors arising from nonlinear factors, kinematic constraints, and environmental influences pose significant challenges. This article introduces a novel compensation method leveraging a joint error propagation deep neural network (JEP-DNN) to overcome these limitations. First, we develop a comprehensive error model integrating both kinematic and nonkinematic factors, including geometrical parameters and gravity-induced deformations. Then, to address the residual model error, we establish a joint error propagation model to describe error propagation across different parts of the robotic system. The JEP-DNN utilizes the error propagation model to perform residual error compensation, combining corrections in joint space and Cartesian space to achieve high interpretability and precision. The experimental results demonstrate the effectiveness of the proposed method. In a laser tracker calibration experiment, the JEP-DNN reduced average position errors by 30.4%, while in a vision-based inspection task for an automotive cornering light, it achieved a 12.4% improvement in positional accuracy compared to uncompensated results. Finally, accuracy and repeatability evaluation of automotive sheet metal parts illustrate that the robotic vision system achieved a maximum position error of 0.143 mm, with high repeatability (less than 0.066 mm).

When measuring fuel assemblies that have just been removed from the reactor core, residual heat in fuel rods causes the uneven temperature gradient field, which subsequently leads to irregular variations of refractive index. Consequently, inevitable thermal turbulence occurs, impacting the measurement accuracy of bow and twist deformation. A multi-layer refractive model is adopted for direct underwater calibration to accurately identify camera parameters. Field simulated turbulence experiments are carried out to qualitatively and quantitatively demonstrate the presence of thermal turbulence. Compared to normal conditions, these image processing experiments show that underwater thermal turbulence induces image fluctuations and temporal fluctuations, affecting image positions and image distributions and ultimately impacting the accuracy of optical line laser center extraction.

The thermal environment in a geostationary orbit is more complicated than a low orbit. Thermal deformation is a primary factor to deteriorate geometric quality of remote imaging of geostationary optical satellites. The on-orbit geometric quality testing of the SDLT-1 satellite of China shows that the light of sight (LOS) deformation in longitude and latitude are 26 pixels and 14 pixels, respectively, which do not meet the accuracy requirements. Therefore, a novel on-orbit geometric calibration method based on star observation is proposed to improve the geometric deformation. The imager embodied stars are employed as sources to evaluate the developed method and compared with the traditional procedure based upon landmarks. The results show that the geometric accuracy of the new approach is 3.2 pixels which is improved compared to traditional calibration method.

Sheet metal parts are frequently used in the industrial field, and the circular holes on these parts are commonly employed as assembly references. Therefore, precise measurement of these circular holes is essential. Because of the scratches, rust, and complex reflection around the edges, the measurement of circular holes on sheet metal parts is a challenging task. In this research, an accurate circular hole inspection method for sheet metal parts is proposed to realize online inspection using a multi-camera system. After capturing images in a single shot, edge detection is adopted to obtain sub-pixel circular hole contours. A new adaptive matching cost based on the descriptor of binary robust independent elementary features and bilinear normalized cross-correlation is introduced to the PatchMatch-based multi-view stereo method to obtain dense and accurate point clouds. To improve the accuracy and integrity of circular hole measurement, a new edge-guided multi-scale geometric consistency matching strategy is proposed accompanied by depth detail restoration. Finally, the points around the circular hole in a spherical range are sampled using the trinocular constraint principle and fitted with the circular equation. The experimental results show that the proposed algorithm performs well, with an error of approximately 0.04 mm, and improves the measurement accuracy compared with the state-of-the-art algorithms.

Improving the accuracy of visual applications for measurement has always been a hot topic in the fields of aerospace, automotive manufacturing and robotics. The multi-camera system, which can provide redundant information from multiple views to improve measurement accuracy and robustness, has been widely used. Due to factors such as pixel dispersion, image noise and mismatches, the extraction accuracy of corresponding key points in images is greatly affected. Traditional 3D point reconstruction algorithms assign the same weight to the corresponding key points in each image, which lacks consideration of the different extraction accuracy of the key points, resulting in lower measurement accuracy. In this paper, weighted multi-camera stereo vision by considering geometric relationships is proposed, which can be extended to the measurement of features with theoretical geometric relationships in projection by any number of cameras. A five-camera measurement system is designed for accuracy verification. The experimental results indicate that by reasonable weight computation, the root-mean-square errors reduce 4.46%, 5.02%, 3.38% for length, width and center distance of rectangle respectively and the root-mean-square errors reduce 3.26%, 10.69%, 2.63% for length, width and center distance of oval shape respectively compared with the least square method. Besides, the measurement robustness is also proved to have a significant improvement.

Finite element simulation and thermal dynamic simulation were employed to investigate the formation feature dimensions, microstructure and mechanical properties of full penetration laser-GMAW welded joints. An ellipsoidal-conical hybrid heat source was established, and the thermal processes of hybrid welding for butt joint on a steel plate with 5, 10 and 14 mm thickness were predicted. The effects of arc power, laser power, and welding speed on the characteristic size at the cross section were investigated. It is found that the arc power only affects the size of upside, the laser power mostly determines the size of backside and the minimum width, and less effect on the width of upside, while the welding speed affects all the characteristic size of cross section. A relationship among phase composition, mechanical properties and cooling rate was established by thermal dynamic simulation. Combining the simulation of welding thermal cycle, the microstructure and mechanical properties of the joints can be predicted. This research is interesting and important for the design of process parameters during welding.

In precision manufacturing and automated inspection, the demand for online measurement of workpieces is continuously increasing in aerospace and equipment manufacturing industries. Because of the high speed and low accumulated error characteristics of parallel mechanisms, this paper proposes a 4-PUU parallel coordinate measuring machine (CMM) for in-line part measurement, and provides its error compensation methods. The position errors are classified into geometric and non-geometric errors. For geometric errors, an error model for geometric error identification is established, and an iterative method is employed for parameter identification. For non-geometric errors, a Kriging model is used for compensation based on the spatial similarity of errors. By combining geometric error modeling with spatial interpolation, an error compensation method for the 4-PUU parallel mechanism is proposed. After compensation, the positional accuracy of the mechanism is significantly improved. The robot’s average absolute positional error is reduced from 3.6905 mm to 0.0810 mm, a decrease of 97.81%. The experimental results show that the proposed method can improve the positional accuracy of the parallel CMM.

In the field of robotic automation, achieving high position accuracy in robotic vision systems (RVSs) is a pivotal challenge that directly impacts the efficiency and effectiveness of industrial applications. This study introduces a comprehensive modeling approach that integrates kinematic and joint compliance factors to significantly enhance the position accuracy of a system. In the first place, we develop a unified kinematic model that effectively reduces the complexity and error accumulation associated with the calibration of robotic systems. At the heart of our approach is the formulation of a joint compliance model that meticulously accounts for the intricacies of the joint connector, the external load, and the self-weight of robotic links. By employing a novel 3D rotary laser sensor for precise error measurement and model calibration, our method offers a streamlined and efficient solution for the accurate integration of vision systems into robotic operations. The efficacy of our proposed models is validated through experiments conducted on a FANUC LR Mate 200iD robot, showcasing notable improvements in the position accuracy of robotic vision system. Our findings contribute a framework for the calibration and error compensation of RVS, holding significant potential for advancements in automated tasks requiring high precision.

The digital speckle pattern (DSP) is an essential component in the speckle projection profilometry (SPP) task, its quality directly affects the results of three-dimensional (3D) shape reconstruction. However, the SPP field lacks specialized numerical metrics for evaluating speckle quality. To address this issue, this study introduces a multi-factor metric (MFM) for comprehensive DSP assessment. Through comparing the metric, optimal parameter ranges for DSP design and the advisable matching subset size can be determined for SPP algorithm. A global indicator named valid feature distribution (VFD) based on scale-invariant feature transform (SIFT) and Delaunay triangulation, is defined to analyze the overall information distribution in DSPs. In addition, MFM incorporates a local metric called mean subset intensity gradient (MSIG), which aids in selecting the suitable radius for different DSPs to balance the accuracy and efficiency. The quality assessment targets the speckle scene images, allowing for the reverse adjustment of the most suitable DSP according to different scenes. The performance of DSPs can be evaluated based on the accuracy and completeness of 3D reconstruction results. By conducting simulation experiments on the 3ds Max platform, the recommended parameter range for DSP can be inferred, including speckle density ratio, speckle diameter, and random variation rate. Appropriate subset sizes for different scenes are also investigated. Furthermore, the MFM is verified on a real binocular speckle device, demonstrating that the measurement standard deviation of a complex workpiece can be reduced to 0.078 mm using the recommended DSP.