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Presently, 6 Degree of Freedom (6DOF) visual pose measurement methods enjoy popularity in the industrial sector. However, challenges persist in accurately measuring the visual pose of blank and rough metal casts. Therefore, this paper introduces a 6DOF pose measurement method utilizing stereo vision, and aims to the 6DOF pose measurement of blank and rough metal casts.

This paper studies the 6DOF pose measurement of metal casts from three aspects: sample enhancement of industrial objects, optimization of detector and attention mechanism. Virtual reality technology is used for sample enhancement of metal casts, which solves the problem of large-scale sample sampling in industrial application. The method also includes a novel deep learning detector that uses multiple key points on the object surface as regression objects to detect industrial objects with rotation characteristics. By introducing a mixed paths attention module, the detection accuracy of the detector and the convergence speed of the training are improved.

The experimental results show that the proposed method has a better detection effect for metal casts with smaller size scaling and rotation characteristics.

A method for 6DOF pose measurement of industrial objects is proposed, which realizes the pose measurement and grasping of metal blanks and rough machined casts by industrial robots.

This paper aims to study six degrees-of-freedom (6DOF) pose measurement of reflective metal casts by machine vision, analyze the problems existing in the positioning of metal casts by stereo vision sensor in unstructured environment and put forward the visual positioning and grasping strategy that can be used in industrial robot cell.

A multikeypoints detection network Binocular Attention Hourglass Net is constructed, which can complete the two-dimensional positioning of the left and right cameras of the stereo vision system at the same time and provide reconstruction information for three-dimensional pose measurement. Generate adversarial networks is introduced to enhance the image of local feature area of object surface, and the three-dimensional pose measurement of object is completed by combining RANSAC ellipse fitting algorithm and triangulation method.

The proposed method realizes the high-precision 6DOF positioning and grasping of reflective metal casts by industrial robots; it has been applied in many fields and solves the problem of difficult visual measurement of reflective casts. The experimental results show that the system exhibits superior recognition performance, which meets the requirements of the grasping task.

Because of the chosen research approach, the research results may lack generalizability. The proposed method is more suitable for objects with plane positioning features.

This paper realizes the 6DOF pose measurement of reflective casts by vision system, and solves the problem of positioning and grasping such objects by industrial robot.

This paper aims to investigate a stereo vision-based hybrid (additive and subtractive) manufacturing process using direct laser metal deposition, computer numerical control (CNC) machining and in-process scanning to repair metallic components automatically. The focus of this work was to realize automated alignment and adaptive tool path generation that can repair metallic components after a single setup.

Stereo vision was used to detect the defect area for automated alignment. After the defect is located, a laser displacement sensor is used to scan the defect area before and after laser metal deposition. The scan is then processed by an adaptive algorithm to generate a tool path for repairing the defect.

The hybrid manufacturing processes for repairing metallic component combine the advantages of free-form fabrication from additive manufacturing with the high-accuracy offered by CNC machining. A Ti-6Al-4V component with a manufacturing defect was repaired by the proposed process. Compared to previous research on repairing worn components, introducing stereo vision and laser scanning dramatically simplifies the manual labor required to extract and reconstruct the defect area’s geometry.

This paper demonstrates an automated metallic component repair process by integrating stereo vision and a laser displacement sensor into a hybrid manufacturing system. Experimental results and microstructure analysis shows that the defect area could be repaired feasibly and efficiently with acceptable heat affected zone using the proposed approach.

Complicated tubes extensively exist in the industrial equipment. The manufacturing precision of the tubes is difficult to be ensured with bending machine. Therefore, the tubes’ 3D geometric error should be fixed according to measurement results. However, there are no convenient methods to accomplish the measurement accurately and effectively. Thus, this paper aims to propose a new tube measurement method to achieve tube's automatic measurement. The accurate measurement results can be used to fix the geometric error of the tube to achieve stress-free assembly.

Tubes’ shape can be determined by control points. First, the point clouds of the centre line by multi-stereo-vision technology are reconstructed. Then, the point clouds to the spine of the tube are thinned by moving least-squares and segmented into lines and arcs. Finally, the control points are calculated and the model is reconstructed. The authors can get the tube’s geometric dimensions from the model.

The experiment results indicate that the multi-stereo-vision technology can solve the occlusion problem and measure the complicated tubes efficiently and accurately.

The paper proposed a tube measurement method. The repeatability measuring precision was 0.12 mm, and the absolute measuring precision was within 0.78 mm. The tube spectra assessed in this paper are in the range of angles between two adjacent line segments of 3-177° and the shortest length of the line segment is greater than 5 mm, confirming that the proposed algorithm can measure various complicated tubes effectively and accurately.

Profile measurement with boundary information plays a vital role in the detection of quality in the assembly of aviation parts. The purpose of this paper is to improve the evaluation accuracy of the aerodynamic shapes of airplanes, the profiles of large-sized parts need to be measured accurately.

In this paper, an accurate profile measurement method based on boundary reference points is proposed for the industrial stereo-vision system. Based on the boundary-reference points, the authors established a priori constraint for extracting the boundary of the measured part. Combining with the image features of background and the measured part, an image-edge compensation model is established to extract the boundary of the measured part. The critical point of a laser stripe on the edge of the measured part is extracted corresponding to the boundary constraint. Finally, as per the principle of binocular vision, the profile of the measured part is reconstructed.

Laboratory experiments validate the measurement accuracy of the proposed method which is 0.33 mm. In the analysis of results between the measured data and the theoretical model, the measuring accuracy of the proposed method was found to be significantly higher than that of the other traditional methods.

An aviation part was measured in the part-assembly shop by the proposed method, which verified the feasibility and effectiveness of this method. The research can realize the measurement of smooth surface boundary which can solve existing profile reconstruction problems for aviation parts.

According to the two-dimensional contour constraint, critical points of the laser strip sequence at the edge of measured part are extracted and the accurate profile reconstruction with the boundary is realized.

This paper aims to review state of the art of techniques for dimensioning chronic wounds, and validate the possibilities of employing a new proposed optical imaging approach for general task of wound assessment.

Current techniques used for quantifying wound surface are reviewed and evaluated from various perspectives to exam their usability in wound care clinical settings. A photometric stereo (PS) approach will be identified and verified to work as an alternative solution to better satisfy practical requirements on quantifying the dimension of real and mocked wounds.

Both contact and contactless approaches provide some useful functions for wound management; however, new imaging modalities are still required for achieving good portability, affordability and applicability in assisting decision-making in clinical settings. The PS approach can work as a potential solution to provide these functionalities as well as dense geometrical and color texture information of measured areas. The experiments demonstrate that the new approach is able to conveniently produce comparable results to those from latest stereo vision-based techniques.

This work proposed and initially verified the potential of PS technique for the task of wound measurement. Substantial improvements on the prototype and more clinical trials are still required to validate the new technique before it is accepted as a tool for practical wound measurement.

This new PS approach has good potential to reliably measure the dimension of wounds as well as recover their color texture which could contain additional valuable information for predicting a healing procedure for those wound occurring deeper underneath the skin surface.

This paper aims to solve the problem of free-form tubes’ machining errors which are caused by their complex geometries and material properties.

In this paper, the authors propose a multi-view vision-based method for measuring free-form tubes. The authors apply photogrammetry theory to construct the initial model and then optimize the model using an energy function. The energy function is based on the features of the image of the tube. Solving the energy function allows to use the gray features of the images to reconstruct centerline point clouds and thus obtain the pertinent geometric parameters.

According to the experiments, the measurement process takes less than 2 min and the precision of the proposed system is 0.2 mm. The authors used simple operations to carry out the measurements, and the process is fully automatic.

This paper proposes a method for measuring free-form tubes based on multi-view vision, which has not been attempted to the best of authors’ knowledge. This method differs from traditional multi-view vision measurement methods, because it does not rely on the data of the design model of the tube. The application of the energy function also avoids the problem of matching corresponding points and thus simplifying the calculation and improving its stability.

Accurately, positioning is a fundamental requirement for vision measurement systems. The calculation of the harvesting width can not only help farmers adjust the direction of the intelligent harvesting robot in time but also provide data support for future unmanned vehicles.

To make the length of each pixel equal, the image is restored to the aerial view in the world coordinate system. To solve the problem of too much calculation caused by too many particles, a certain number of particles are scattered near the crop boundary and the distribution regularities of particles’ weight are analyzed. Based on the analysis, a novel boundary positioning method is presented. In the meantime, to improve the robustness of the algorithm, the back-projection algorithm is also used for boundary positioning.

Experiments demonstrate that the proposed method could well meet the precision and real-time requirements with the measurement error within 55 mm.

In visual target tracking, using particle filtering, a rectangular is used to track the target and cannot obtain the boundary information. This paper studied the distribution of the particle set near the crop boundary and proposed an improved particle filtering algorithm. In the algorithm, a small amount of particles is used to determine the crop boundary and accurate positioning of the crop boundary is realized.

This study aims to develop an optimized 3D laser point reconstruction using Descent Gradient algorithm. Precise and accurate reconstruction of 3D laser point cloud of the complex environment/object is a key solution for many industries such as construction, gaming, automobiles, aerial navigation, architecture and automation. A 2D laser scanner along with a servo motor/pan tilt/inertial measurement unit is used for generating 3D point cloud (either environment/object or both) by acquiring the real-time data from sensors. However, while generating the 3D laser point cloud, various problems related to time synchronization problem between laser and servomotor and torque variation in servomotors arise, which causes misalignment in stacking the 2D laser scan for generating the 3D point cloud of the environment. Because of the misalignment in stacking, the 2D laser scan corresponding to the erroneous angular and position information by the servomotor and the 3D laser point cloud become distorted in terms of inconsistency for measuring the dimension of the objects.

This paper addresses a modified 3D laser system assembled from a 2D laser scanner coupled with a servomotor (dynamixel motor) for developing an efficient 3D laser point cloud with the implementation of an optimization technique: descent gradient filter (DGT). The proposed approach reduces the cost function (error) in the angular and position coordinates of the servo motor caused because of torque variation and time synchronization, which resulted in enhancing the accuracy in 3D point cloud mapping for the accurate measurement of the object’s dimensions.

Various real-world experiments are performed with the proposed DGT filter linked with laser scanner and servomotor and an improvement of 6.5 per cent in measuring the accurate dimension of object is obtained while comparing with conventional approaches for generating a 3D laser point cloud.

This proposed technique may be applicable for various industrial applications that are based on robotics arms (such as painting, welding and cutting) in the automobile industry, the optimized measurement of object, efficient mobile robot navigation, precise 3D reconstruction of environment/object in construction, architecture applications, airborne applications and aerial navigation.

This paper aims to propose a reasonable method to evaluate uncertainty of measurement of industrial robots’ orientation repeatability and solve the non-linear problem existing in its evaluation procedure.

Firstly, a measurement model of orientation repeatability, based on laser tracker, is established. Secondly, some factors, influencing the measurement result of orientation repeatability, are identified, and their probability distribution functions are modelled. Thirdly, based on Monte Carlo method, an uncertainty evaluation model and algorithm of measurement of industrial robot’s orientation repeatability are built. Finally, an industrial robot is taken as the research object to validate the rationality of proposed method.

Results show that the measurement model of orientation repeatability of industrial robot is non-linear, and the proposed method can reasonably and objectively estimate uncertainty of measurement of industrial robots’ orientation repeatability.

This paper, based on Monte Carlo method and experimental work, proposes an uncertainty evaluation method of measurement of industrial robots’ orientation repeatability which can solve the non-linear problem and provide a reasonable and objective evaluation. And the stochastic ellipsoid approach is firstly taken to model the repeatability of laser tracker. Additionally, this research is beneficial to decide whether the orientation repeatability of the industrial robot meets its requirements.