Pose Measurement Method Research Articles

Retraction Note: A full freedom pose measurement method for industrial robot based on reinforcement learning algorithm

Retraction Note: A full freedom pose measurement method for industrial robot based on reinforcement learning algorithm

Pose Measurement Method Based on Machine Vision and Novel Directional Target

Aiming at the shortcomings of the existing machine vision pose measurement technology, a pose measurement method based on monocular vision and a cooperative target is proposed. A planar target designed with circles and rings as the main body is dedicated to object pose measurement, and a feature point coordinate extraction and sorting algorithm is designed for this target to effectively extract image features on the target. The RANSAC algorithm and topology-based fitting for the intersection method are used to optimise data processing, further improving the accuracy of feature point coordinate extraction and ultimately achieving high-precision measurement of object poses. The experimental results show that the measurement accuracy of the roll angle perpendicular to the optical axis can reach 0.02°, and the repeatability can reach 0.0004° after removing the systematic error; the measurement accuracy of the pitch angle can reach 0.03°, and the repeatability can go to 0.002° after removing the systematic error. The measurement range of the pitch angle is [−30°, +30°]; the measurement range of the roll angle is [−179°, +179°]. The experimental results show that the system has high measurement accuracy and meets the requirements of high-precision measurement.

Rectangular Natural Feature Recognition and Pose Measurement Method for Non-Cooperative Spacecraft

Accurately estimating the pose of spacecraft is indispensable for space applications. However, such targets are generally non-cooperative, i.e., no markers are mounted on them, and they include no parts for operation. Therefore, the detection and measurement of a non-cooperative target is very challenging. Stereovision sensors are important solutions in the near field. In this paper, a rectangular natural feature recognition and pose measurement method for non-cooperative spacecraft is proposed. Solar panels of spacecraft were selected as detection objects, and their image features were captured via stereo vision. These rectangle features were then reconstructed in 3D Cartesian space through parallelogram fitting on the image planes of two cameras. The vertexes of rectangle features were detected and used to solve the pose of a non-cooperative target. An experimental system was built to validate the effectiveness of the algorithm. The experimental results show that the average position measurement error of the algorithm is about 10 mm and the average attitude measurement error is less than 1°. The results also show that the proposed method achieves high accuracy and efficiency.

A 6DOF pose measurement method for metal casts object based on stereo vision sensor

Purpose 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. Design/methodology/approach 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. Findings The experimental results show that the proposed method has a better detection effect for metal casts with smaller size scaling and rotation characteristics. Originality/value 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.

Kinematics Parameter Calibration of Serial Industrial Robots Based on Partial Pose Measurement

The kinematics parameter error is the main error factor that affects the absolute accuracy of industrial robots. The absolute accuracy of industrial robots can be effectively improved through kinematics calibration. The error model-based method is one of the main methods for calibrating the kinematics parameter error. This paper presents a kinematics parameter calibration method for serial industrial robots based on partial pose measurement. Firstly, the kinematics and the pose error models have been established based on the modified Denavit–Hartenberg (MDH) model. By introducing the concept of error sensitivity, the average significance index is proposed to quantitatively analyze the effects of the kinematics parameter error on the pose error of a robot. The results show that there is no need to measure the full pose error of the robot. Secondly, a partial pose measurement device and method have been presented. The proposed device can measure the position error and the attitude error on the x-axis or y-axis. Finally, the full pose error model, the NP-type partial pose error model, and the OP-type partial pose error model have been applied for calibrating the kinematics parameter errors. The experimental results show that the effectiveness of the OP-type partial pose error model is consistent with the full pose error model.

Polarized light vision pose measurement method by fusing the constraints provided between control points for hand-eye calibration

The accuracy of hand-eye calibration is determined by the pose measurement accuracy of the calibration target. In the hand-eye calibration of industrial robots, the pose measurement can be performed based on the polarization information to filter out high-brightness areas of the image, optimize image processing, and increase effective control points. However, it is hard to decide on an adequate polarization angle. An inadequate polarization angle will affect the image contrast and ultimately affect the extraction accuracy of the 2D projections of the control points. In addition, the existing non-iterative pose measurement methods only consider the constraints provided by the single control points. The constraints provided between the control points are not in consideration. This results in reduced pose measurement accuracy in noisy environments. Aiming at the above problems, a polarized light vision pose measurement method by fusing the constraints provided between control points for hand-eye calibration is proposed: the adequate polarization angle was determined based on the cross-ratio invariance, and the target image collected under the adequate polarization angle was used for subsequent pose measurement to reduce the high-brightness areas on the premise of ensuring the image contrast. We introduced the plane created by two control points and the optical center to represent the constraints provided between these two control points. In this way, the constraints provided by any two control points can be fused with the constraints provided by the single control points to solve the pose non-iteratively. Compared to other current methods, there are more significant increases in our method's pose measurement accuracy and stability. The experimental results show that the angle measurement error is less than 0.052° over the measuring range of -60°-+60°, and the displacement measurement error is less than 0.01 mm over the measuring range of 0-20 mm.

A full 6 degree-of-freedom visual estimation with laser assisted

Efficient and accurate measurement in unstructured environment is a challenging issue and also research interest in the industrial field. The accuracy of vision is very high in the close-range scene and the stability of laser measurement is better, with a large achievable range. A 6-DOF pose measurement method based on vision and assisted by laser is proposed in this paper. By combining their respective advantages, the method can be immune to ambient light. The optimization objective is established by using the projection coordinates of laser spots in different coordinate systems and solved by Newton–Raphson iteration. The initial value is estimated by Kronecker product. The Monte Carlo method is further used to simulate. The causes of measurement errors and the influence factors affecting the accuracy are analyzed. On this basis, an experimental prototype is constructed. The accuracy and repeatability accuracy experiments are conducted to verify the correctness of the proposed method.

Binocular vision and priori data based intelligent pose measurement method of large aerospace cylindrical components

Binocular vision and priori data based intelligent pose measurement method of large aerospace cylindrical components

A High-Precision Real-Time Pose Measurement Method for the Primary Lens of Large Aperture Space Telescope Based on Laser Ranging.

The aperture of space telescopes increases with their required resolution, and the transmission optical systems with long focal length and diffractive primary lens are becoming increasingly popular. In space, the changes in the pose of the primary lens relative to the rear lens group have a significant impact on the imaging performance of the telescope system. The measurement of the pose of the primary lens in real-time and with high-precision is one of the important techniques for a space telescope. In this paper, a high-precision real-time pose measurement method for the primary lens of a space telescope in orbit based on laser ranging is proposed, and a verification system is established. The pose change of the telescope's primary lens can be easily calculated through six high-precision laser distance changes. The measurement system can be installed freely, which solves the problems of complex system structure and low measurement accuracy in traditional pose measurement techniques. Analysis and experiments show that this method can accurately obtain the pose of the primary lens in real-time. The rotation error of the measurement system is 2 × 10-5 degrees (0.072 arcsecs), and the translation error is 0.2 μm. This study will provide a scientific basis for high-quality imaging of a space telescope.

Online measurement method for assembly pose of gear structure based on monocular vision

The gear structure is an important part of the transmission device. The majority of manual methods are currently used to complete the assembly of the large internal gear. Manual assembly is difficult and inefficient. Therefore, an online measurement method for the assembly pose of the gear structure based on monocular vision is proposed. After the critical features of the gear structure have been detected, a duality elimination method based on traversal mapping dots is proposed to obtain the correct solution for the spatial circle pose. Concurrently, the circle pose optimization model is established to enhance pose precision. Then, a new calibration board was designed to complete the hand-eye calibration of the parallel mechanism and camera. Finally, the correction amount required to complete the internal and external teeth assembly is calculated based on the iterative update of the pose measurement method. The experimental results show that the comprehensive accuracy of the pose measurement method exceeds 0.2 mm, the average assembly time is approximately 14 min and the assembly success rate is approximately 97%. It has been realized that simulated gear structure parts can be assembled automatically.

Vision and Inertial Navigation Combined-Based Pose Measurement Method of Cantilever Roadheader

Pose measurement of coal mine excavation equipment is an important part of roadway excavation. However, in the underground mining roadway of coal mine, there are some influencing factors such as low illumination, high dust and interference from multiple equipment, which lead to the difficulty in the position and pose measurement of roadheader with low measurement accuracy and poor stability. A combination positioning method based on machine vision and optical fiber inertial navigation is proposed to realize the position and pose measurement of roadheader and improve the accuracy and stability of the position and pose measurement. The visual measurement model of arm roadheader is established, and the optical fiber inertial navigation technology and the spatial coordinate transformation method are used. Finally, the Kalman filter fusion algorithm is used to fuse the two kinds of data to get the accurate roadheader pose data, and the inertia is compensated and corrected. Underground coal mine experiments are designed to verify the performance of the proposed method. The results show that the positioning error of the roadheader body using this method is within 40 mm, which meets the positioning accuracy requirements of roadway construction. This method compensates for the shortcomings of low accuracy and poor reliability of single vision measurement, single inertial navigation measurement and single odometer measurement.

High-precision pose measurement method based on binocular vision in dark lighting environments

Measuring the pose of non-cooperative targets in space is a critical supporting technology for cleaning up space debris and recovering items. However, most existing methods are simulation experiments conducted in good lighting environments and tend to show poor performance in dark lighting environments. A target pose measurement method based on binocular vision is proposed, which is suitable for dark lighting environments. First, the traditional features from accelerated segment test algorithm are improved to reduce the influence of illumination on the performance of feature point extraction under various postures. The point feature and line feature are combined to extract image features more easily in a dark lighting environment while retaining the high accuracy of the pose measurement algorithm based on point features. Second, the normalized cross-correlation coefficient matching method is combined with the epipolar constraint to narrow the search range of the matching points from the two-dimensional plane to the epipolar line, which substantially improves the matching efficiency and accuracy of the matching algorithm. Finally, post-processing through feature matching is performed to reduce the probability of mismatches. Simulation and physical experiment results show that our method can stably extract features and obtain high-precision target pose information in well-illuminated as well as dark lighting environments, making it suitable for high-precision target pose measurement under insufficient illumination.

Pose measurement and motion estimation of non-cooperative satellite based on spatial circle feature

Malfunctioned satellites have seriously threatened orbital safety, and the capture of these satellites is of great significance. The pose measurement and the motion estimation of the tumbling satellite is the premise of capture. In this paper, the docking ring of the satellite is identified, which is equivalent to a spatial circle. Combined with the nozzle feature, the pose duality of the spatial circle can be eliminated. And the measurement accuracy is improved by minimizing the reprojection error of the docking ring and the nozzle. Due to the symmetry of the docking ring, the measured pose has only five degrees of freedom, losing the degree of freedom of rotation around the normal vector. In the motion estimation algorithm, the observability of the tumbling motion is firstly analyzed, then an error-state Kalman filter with inertia ratio constraints is designed. To improve the convergence speed and stability of the filter, a rough estimation algorithm of filter initial value based on linear term extraction and particle swarm optimization is proposed. The effectiveness of the pose measurement and motion estimation method is verified by simulations.

An accurate pose measurement method of workpiece based on rapid extraction of local feature points

An accurate pose measurement method of workpiece based on rapid extraction of local feature points

A low-cost and real-time pose measurement method for straight pipe jacking machine based on dual-screen laser target in tunneling guidance

In this paper, a low-cost and real-time pose measurement method for straight pipe jacking machine in tunneling guidance is proposed, which utilizes an instruction laser and a dual-screen laser target. The precise localization of light-spot on target is realized by high-performance processing of the image and grid calibration of the target. The spatial matrix transformation method is used to calculate the parameters for the specified position of the pipe jacking machine. Subsequently, the horizontal/vertical deviation between the actual moving route of the straight pipe jacking machine and the design tunnel axis (DTA) can be calculated. The results of the experiment show that the precision and absolute error of the azimuth angle measurement are better than 0.03° (1∑) and 0.05°, respectively. The measurement error of horizontal/vertical deviation is less than 3 mm. Hence, this method is feasible and highly accurate, which can effectively solve the guidance problem of pipe jacking construction.

Position and Pose Measurement of 3-PRS Ankle Rehabilitation Robot Based on Deep Learning

Introduction: The position and pose measurement of the rehabilitation robot plays a very important role in patient rehabilitation movement, and the non-contact real-time robot position and pose measurement is of great significance. Rehabilitation training is a relatively complicated process, so it is very important to detect the training process of the rehabilitation robot in real time and accuracy. The method of the deep learning has a very good effect on monitoring the rehabilitation robot state. Methods: The structure sketch and the 3D model of the 3-PRS ankle rehabilitation robot are established, and the mechanism kinematics is analyzed to obtain the relationship between the driving input - the three slider heights - and the position and pose parameters. The whole network of the position and pose measurement is composed of two stages: (1) measuring the slider heights using the CNN based on the robot image and (2) calculating the position and pose parameter using the BPNN based on the measured slider heights from the CNN. According to the characteristics of continuous variation of the slider heights, a regression CNN is proposed and established to measure the robot slider height. Based on the data calculated by using the inverse kinematics of the 3-PRS ankle rehabilitation robot, a BPNN is established to solve the forward kinematics for the position and pose. Results: The experimental results show that the regression CNN outputs the slider height and then the BPNN accurately outputs the corresponding position and pose. Eventually, the position and pose parameters are obtained from the robot image. Compared with the traditional robot position and pose measurement method, the proposed method has significant advantages. Conclusion: The proposed 3-PRS ankle rehabilitation position and pose method can not only shorten the experiment period and cost, but also get excellent timeliness and precision. The proposed approach can help the medical staff to monitor the status of the rehabilitation robot and help the patient rehabilitation in training. Discussion: The goal of the work is to construct a new position and pose detection network based on the combination of the regression convolutional neural network (CNN) and the back propagation neural network (BPNN). The main contribution is to measure the position and pose of the 3-PRS ankle rehabilitation robot in real time, which improves the measurement accuracy and the efficiency of the medical staff work.

Pose Determination of the Disc Cutter Holder of Shield Machine Based on Monocular Vision.

The visual measurement system plays a vital role in the disc cutter changing robot of the shield machine, and its accuracy directly determines the success rate of the disc cutter grasping. However, the actual industrial environment with strong noise brings a great challenge to the pose measurement methods. The existing methods are difficult to meet the required accuracy of pose measurement based on machine vision under the disc cutter changing conditions. To solve this problem, we propose a monocular visual pose measurement method consisting of the high precision optimal solution to the PnP problem (OPnP) method and the highly robust distance matching (DM) method. First, the OPnP method is used to calculate the rough pose of the shield machine’s cutter holder, and then the DM method is used to measure its pose accurately. Simulation results show that the proposed monocular measurement method has better accuracy and robustness than the several mainstream PnP methods. The experimental results also show that the maximum error of the proposed method is 0.28° in the direction of rotation and 0.32 mm in the direction of translation, which can meet the measurement accuracy requirement of the vision system of the disc cutter changing robot in practical engineering application.

Rapid Position and Pose Measurement Method for Segment Girder Formwork in Precast Girder Yard

Rapid Position and Pose Measurement Method for Segment Girder Formwork in Precast Girder Yard

Research on linear pose measurement method based on optimal polarization angle

Research on linear pose measurement method based on optimal polarization angle

Real-Time Accurate Deep Learning-Based Edge Detection for 3-D Pantograph Pose Status Inspection

As an important part of the online monitoring in electrified railway, pantograph operational pose inspection plays a vital role in pantograph and catenary system (PCS) security. Due to the impact of the complex conditions on the train roof, it is difficult to replace the existing video surveillance with fully intelligent operational pose inspection. Therefore, this article proposes a novel accurate monocular 3-D pantograph horn edge-based pantograph pose inspection method in the power of superior ability of deep learning. It mainly includes two steps: edge detection and pose optimization. First, we highlight a brand-new high-precision subpixel edge prediction network. The downsampling heatmap and offset prediction map in our edge detector are joined to predict full-size edge points and thus improve the accuracy to the subpixel level. In addition, our newly proposed numerical gradient smoothing loss function overcomes the “jaggy” phenomenon of the edge curves. Second, we propose a nonpoint-to-point corresponding 3-D pose measurement method of the monocular camera. In the iterative optimized process, we innovatively apply the chamfer distance as an optimization function to dynamically adjust the correspondence between the reprojection points and the predicted edge points to optimal convergence. Ablation and comparative experiments demonstrate that our method achieves excellent performance (F-score 93.0% in FEP_2.0 (fixed edge probability) with 82 frames per second (FPS), overcoming the complex conditions of the practical online monitoring sequences. The actual pose measurement results further confirm our efficiency, thereby achieving a robust real-time and high-precision monocular 3-D pantograph operating pose inspection.