Search

Marker-less hand–eye calibration permits the acquisition of an accurate transformation between an optical sensor and a robot in unstructured environments. Single monocular cameras, despite their low cost and modest computation requirements, present difficulties for this purpose due to their incomplete correspondence of projected coordinates. In this work, we introduce a hand–eye calibration procedure based on the rotation representations inferred by an augmented autoencoder neural network. Learning-based models that attempt to directly regress the spatial transform of objects such as the links of robotic manipulators perform poorly in the orientation domain, but this can be overcome through the analysis of the latent space vectors constructed in the autoencoding process. This technique is computationally inexpensive and can be run in real time in markedly varied lighting and occlusion conditions. To evaluate the procedure, we use a color-depth camera and perform a registration step between the predicted and the captured point clouds to measure translation and orientation errors and compare the results to a baseline based on traditional checkerboard markers. [ABSTRACT FROM AUTHOR]

In the process of industrial production, manual assembly of workpieces exists with low efficiency and high intensity, and some of the assembly process of the human body has a certain degree of danger. At the same time, traditional machine learning algorithms are difficult to adapt to the complexity of the current industrial field environment; the change in the environment will greatly affect the accuracy of the robot's work. Therefore, this paper proposes a method based on the combination of machine vision and the YOLOv5 deep learning model to obtain the disk porous localization information, after coordinate mapping by the ROS communication control robotic arm work, in order to improve the anti-interference ability of the environment and work efficiency but also reduce the danger to the human body. The system utilizes a camera to collect real-time images of targets in complex environments and, then, trains and processes them for recognition such that coordinate localization information can be obtained. This information is converted into coordinates under the robot coordinate system through hand–eye calibration, and the robot is then controlled to complete multi-hole localization and tracking by means of communication between the upper and lower computers. The results show that there is a high accuracy in the training and testing of the target object, and the control accuracy of the robotic arm is also relatively high. The method has strong anti-interference to the complex environment of industry and exhibits a certain feasibility and effectiveness. It lays a foundation for achieving the automated installation of docking disk workpieces in industrial production and also provides a more favorable choice for the production and installation of the process of screw positioning needs. [ABSTRACT FROM AUTHOR]

In the functioning of the hand–eye collaboration of an apple picking robot, the accuracy of the hand–eye relationship is a key factor affecting the efficiency and accuracy of the robot's operation. In order to enhance the low accuracy of traditional hand–eye calibration methods, linear and nonlinear solving methods based on mathematical tools such as quaternions are commonly adopted. To solve the loss of accuracy in decoupling during the linearization solution and to reduce the cumulative error that occurs during nonlinear solutions, a hand–eye calibration method, based on the ICP algorithm, is proposed in this paper. The method initializes the ICP matching algorithm with a solution derived from Tsai–Lenz, and substitutes it for iterative computation, thereby ascertaining a precise hand–eye conversion relationship by optimizing the error threshold and iteration count in the ICP matching process. Experimental results demonstrate that the ICP-based hand–eye calibration optimization algorithm not only circumvents the issues pertaining to accuracy loss and significant errors during solving, but also enhances the rotation accuracy by 13.6% and the translation accuracy by 2.47% compared with the work presented by Tsai–Lenz. [ABSTRACT FROM AUTHOR]

With the increasing automation of mobile phone assembly, industrial robots are gradually being used in production lines for loading and unloading operations. At present, industrial robots are mainly used in online teaching mode, in which the robot's movement and path are set by teaching in advance and then repeat the point-to-point operation. This mode of operation is less flexible and requires high professionalism in teaching and offline programming. When positioning and grasping different materials, the adjustment time is long, which affects the efficiency of production changeover. To solve the problem of poor adaptability of loading robots to differentiated products in mobile phone automatic assembly lines, it is necessary to quickly adjust the positioning and grasping of different models of mobile phone middle frames. Therefore, this paper proposes a highly adaptive grasping and positioning method for vision-guided right-angle robots. [ABSTRACT FROM AUTHOR]

For a robot to pick up an object viewed by a camera, the object's position in the image coordinate system must be converted to the robot coordinate system. Recently, a neural network-based method was proposed to achieve this task. This methodology can accurately convert the object's position despite errors and disturbances that arise in a real-world environment, such as the deflection of a robot arm triggered by changes in the robot's posture. However, this method has some drawbacks, such as the need for significant effort in model selection, hyperparameter tuning, and lack of stability and interpretability in the learning results. To address these issues, a method involving linear and nonlinear regressions is proposed. First, linear regression is employed to convert the object's position from the image coordinate system to the robot base coordinate system. Next, B-splines-based nonlinear regression is applied to address the errors and disturbances that occur in a real-world environment. Since this approach is more stable and has better calibration performance with interpretability as opposed to the recent method, it is more practical. In the experiment, calibration results were incorporated into a robot, and its performance was evaluated quantitatively. The proposed method achieved a mean position error of 0.5 mm, while the neural network-based method achieved an error of 1.1 mm. [ABSTRACT FROM AUTHOR]

This paper studies the effect of reference frame selection in sensor-to-sensor extrinsic calibration when formulated as a motion-based hand–eye calibration problem. As the sensor trajectories typically contain some composition of noise, the aim is to determine which selection strategies work best under which noise conditions. Different reference selection options are tested under varying noise conditions in simulations, and the findings are validated with real data from the KITTI dataset. The study is conducted for four state-of-the-art methods, as well as two proposed cost functions for nonlinear optimization. One of the proposed cost functions incorporates outlier rejection to improve calibration performance and was shown to significantly improve performance in the presence of outliers, and either match or outperform the other algorithms in other noise conditions. However, the performance gain from reference frame selection was deemed larger than that from algorithm selection. In addition, we show that with realistic noise, the reference frame selection method commonly used in the literature, is inferior to other tested options, and that relative error metrics are not reliable for telling which method achieves best calibration performance. [ABSTRACT FROM AUTHOR]

The measurement system proposed in this paper, using a measuring arm and line structured light, has a wide range of applications. To improve the scanning efficiency, the system outlined in this paper uses two single-line structured lights to form crosshair structured light, which we combine with a measuring arm to form a comprehensive scanning measurement system. The calibration method of Zhengyou Zhang and a calibration board are used to complete parameter calibration of the sensors and cameras, as well as hand–eye calibration of the measuring arm. For complex curved-surface objects, this system extracts the cross-line structured light optical center location, which suffers from ambiguity. Therefore, we introduce the use of periodic control of the two line structured light sources in order to resolve the light extraction polysemy. Our experimental results indicate that the proposed system can effectively satisfy the function of crosshair structured light scanning of large, complex surfaces. [ABSTRACT FROM AUTHOR]

Multi-line LiDAR and GPS/IMU are widely used in autonomous driving and robotics, such as simultaneous localization and mapping (SLAM). Calibrating the extrinsic parameters of each sensor is a necessary condition for multi-sensor fusion. The calibration of each sensor directly affects the accurate positioning control and perception performance of the vehicle. Through the algorithm, accurate extrinsic parameters and a symmetric covariance matrix of extrinsic parameters can be obtained as a measure of the confidence of the extrinsic parameters. As for the calibration of LiDAR-GPS/IMU, many calibration methods require specific vehicle motion or manual calibration marking scenes to ensure good constraint of the problem, resulting in high costs and a low degree of automation. To solve this problem, we propose a new two-step self-calibration method, which includes extrinsic parameter initialization and refinement. The initialization part decouples the extrinsic parameters from the rotation and translation part, first calculating the reliable initial rotation through the rotation constraints, then calculating the initial translation after obtaining a reliable initial rotation, and eliminating the accumulated drift of LiDAR odometry by loop closure to complete the map construction. In the refinement part, the LiDAR odometry is obtained through scan-to-map registration and is tightly coupled with the IMU. The constraints of the absolute pose in the map refined the extrinsic parameters. Our method is validated in the simulation and real environments, and the results show that the proposed method has high accuracy and robustness. [ABSTRACT FROM AUTHOR]

Many approaches have been developed to solve the hand–eye calibration problem. The traditional approach involves a precise mathematical model, which has advantages and disadvantages. For example, mathematical representations can provide numerical and quantitative results to users and researchers. Thus, it is possible to explain and understand the calibration results. However, information about the end-effector, such as the position attached to the robot and its dimensions, is not considered in the calibration process. If there is no CAD model, additional calibration is required for accurate manipulation, especially for a handmade end-effector. A neural network-based method is used as the solution to this problem. By training a neural network model using data created via the attached end-effector, additional calibration can be avoided. Moreover, it is not necessary to develop a precise and complex mathematical model. However, it is difficult to provide quantitative information because a neural network is a black box. Hence, a method with both advantages is proposed in this study. A mathematical model was developed and optimized using the data created by the attached end-effector. To acquire accurate data and evaluate the calibration results, a tablet computer was utilized. The established method achieved a mean positioning error of 1.0 mm. [ABSTRACT FROM AUTHOR]

The vision system provides an important way for construction robots to obtain the type and spatial location information of the object. The characteristics of the construction environment, construction object, and robot structure are jointly examined in this paper to propose an approach of object segmentation by spraying the robot based on multi-layer perceptron. Firstly, the hand-eye system experimental platform is built through establishing the mathematical model of the system and calibrating the parameters of the model. Secondly, effort is made to carry out research on image preprocessing algorithms and related experiments, and compare the effects of different binocular stereo-matching algorithms in the actual engineering environment. Finally, research and an experiment are conducted to identify the applicability and effect of the depth image object segmentation algorithm based on multi-layer perceptron. The experimental results prove that the application of multi-layer perceptron to object segmentation by spraying robots can meet the requirement on solution accuracy and is suitable for the object segmentation of complex projects in real life. This approach not only overcomes the shortcomings of the existing recognition methods that are poor in accuracy and difficult to be used widely, but also provides basic data for the subsequent three-dimensional reconstruction, thus making a significant contribution to the research of image processing by spraying robots. [ABSTRACT FROM AUTHOR]

This paper presents a modified model for hand–eye calibration based on dual quaternion algebra. By using dual quaternions to represent the rotations and translations of a rigid body simultaneously in the task space, the formulation is elegant for the analysis of the hand–eye equation. The hand–eye transformation derived in this study is represented in a compact manner, which uses a combination of the dual part and the real part of the dual quaternion. Although the hand–eye equation can be solved by using six elements of a dual quaternion without using its scalar parts, the scaler numbers in both the real and dual parts of a dual quaternion contain part of the pose information. The originality is based on the derivation of the construct of the identification algorithm of external parameters of the camera by using all eight elements of a dual quaternion. Then, the data transformation between the cameras of the dual-arm hand–eye robot system is presented. The corresponding results demonstrate that the proposed hand–eye calibration algorithm can process measurement data with noise and can also improve the identification accuracy to verify its efficiency. [ABSTRACT FROM AUTHOR]

Pedicle screw insertion with robot assistance dramatically improves surgical accuracy and safety when compared with manual implantation. In developing such a system, hand–eye calibration is an essential component that aims to determine the transformation between a position tracking and robot-arm systems. In this paper, we propose an effective hand–eye calibration method, namely registration-based hand–eye calibration (RHC), which estimates the calibration transformation via point set registration without the need to solve the AX = XB equation. Our hand–eye calibration method consists of tool-tip pivot calibrations in two-coordinate systems, in addition to paired-point matching, where the point pairs are generated via the steady movement of the robot arm in space. After calibration, our system allows for robot-assisted, image-guided pedicle screw insertion. Comprehensive experiments are conducted to verify the efficacy of the proposed hand–eye calibration method. A mean distance deviation of 0.70 mm and a mean angular deviation of 0.68° are achieved by our system when the proposed hand–eye calibration method is used. Further experiments on drilling trajectories are conducted on plastic vertebrae as well as pig vertebrae. A mean distance deviation of 1.01 mm and a mean angular deviation of 1.11° are observed when the drilled trajectories are compared with the planned trajectories on the pig vertebrae. [ABSTRACT FROM AUTHOR]

Although robotic vision systems offer a promising technology solution for rapid and reconfigurable in-process 3D inspection of complex and large parts in contemporary manufacturing, measurement accuracy poses a challenge for its wide deployment. One of the key issues in adopting a robotic vision system is to understand the extent of its measurement errors which are directly correlated with the calibration process. In this paper, a possible source of practical and inherent measurement uncertainties involved in the calibration process of a robotic vision system are discussed. The system considered in this work consists of an image sensor mounted on an industrial robot manipulator with six degrees of freedom. Based on a series of experimental tests and computer simulations, the paper gives a comprehensive performance comparison of different calibration approaches and shows the impact of measurement uncertainties on the calibration process. It has been found from the error sensitivity analysis that minor uncertainties in the calibration process can significantly affect the accuracy of the robotic vision system. Further investigations suggest that inducing errors in image calibration patterns can have an adverse effect on the hand–eye calibration process compared to the angular errors in the robot joints. [ABSTRACT FROM AUTHOR]

To solve the problem of inflexibility of offline hand–eye calibration in "eye-in-hand" modes, an online hand–eye calibration method based on the ChArUco board is proposed in this paper. Firstly, a hand–eye calibration model based on the ChArUco board is established, by analyzing the mathematical model of hand–eye calibration, and the image features of the ChArUco board. According to the advantages of the ChArUco board, with both the checkerboard and the ArUco marker, an online hand–eye calibration algorithm based on the ChArUco board is designed. Then, the online hand–eye calibration algorithm, based on the ChArUco board, is used to realize the dynamic adjustment of the hand–eye position relationship. Finally, the hand–eye calibration experiment is carried out to verify the accuracy of the hand–eye calibration based on the ChArUco board. The robustness and accuracy of the proposed method are verified by online hand–eye calibration experiments. The experimental results show that the accuracy of the online hand–eye calibration method proposed in this paper is between 0.4 mm and 0.6 mm, which is almost the same as the offline hand–eye calibration accuracy. The method in this paper utilizes the advantages of the ChArUco board to realize online hand–eye calibration, which improves the flexibility and robustness of hand–eye calibration. [ABSTRACT FROM AUTHOR]

In view of the phenomenon that camera imaging will appear large up close and small from afar in the eye-to-hand hand-calibration system, one hand–eye calibration is carried out. The manipulator is only suitable for grasping objects of the same height, and the calibration results cannot be applied to grasping products with variable height. Based on the study of the pinhole camera model and the rigid transformation model between coordinate systems, the introduction of the calibration height parameters, the relationship between parameters of the rigid transformation matrix between image the coordinate system and the robot coordinate system, and sampling height are established. In the experiment, firstly, through the calibration of camera parameters, the influence of camera distortion on imaging quality is eliminated, and the influence of calibration height is ignored. Then, the machine coordinate system and image coordinate system of the calibration plate at different heights are calibrated using the four-point calibration method. The parameters of the rigid transformation matrix at different heights (H) are calculated. Finally, through experimental analysis, the high linear relationship between the parameters of the rigid transformation matrix from the image coordinate system to the robot coordinate system and the calibration height is fitted. By analyzing the random error of the experiment, the linear relationship between calibration height and pixel density is further established, and the systematic error of the experimental process is deeply analyzed. The experimental results show that the hand–eye calibration system based on this linear relationship is precise and suitable for grabbing products of any height, and the positioning error is less than 0.08%. [ABSTRACT FROM AUTHOR]

The Euclidean distance error of calibration results cannot be calculated during the hand-eye calibration process of a manipulator because the true values of the hand-eye conversion matrix cannot be obtained. In this study, a new method for error analysis and algorithm optimization is presented. An error analysis of the method is carried out using a priori knowledge that the location of the augmented reality markers is fixed during the calibration process. The coordinates of the AR marker center point are reprojected onto the pixel coordinate system and then compared with the true pixel coordinates of the AR marker center point obtained by corner detection or manual labeling to obtain the Euclidean distance between the two coordinates as the basis for the error analysis. We then fine-tune the results of the hand-eye calibration algorithm to obtain the smallest reprojection error, thereby obtaining higher-precision calibration results. The experimental results show that, compared with the Tsai-Lenz algorithm, the optimized algorithm in this study reduces the average reprojection error by 44.43% and the average visual positioning error by 50.63%. Therefore, the proposed optimization method can significantly improve the accuracy of hand-eye calibration results.

Featured Application: This study aims to improve the absolute position error of robot manipulators for vehicle assembly line without using an expensive external apparatus. It is well known that most of the industrial robots have excellent repeatability in positioning. However, the absolute position errors of industrial robots are relatively poor, and in some cases the error may reach even several millimeters, which make it difficult to apply the robot system to vehicle assembly lines that need small position errors. In this paper, we have studied a method to reduce the absolute position error of robots using machine vision and neural network. The position/orientation of robot tool-end is compensated using a vision-based approach combined with a neural network, where a novel indirect calibration approach is presented in order to gather information for training the neural network. In the simulation, the proposed compensation algorithm was found to reduce the positional error to 98%. On average, the absolute position error was 0.029 mm. The application of the proposed algorithm in the actual robot experiment reduced the error to 50.3%, averaging 1.79 mm. [ABSTRACT FROM AUTHOR]

For autonomous driving, it is important to obtain precise and high-frequency localization information. This paper proposes a novel method in which the Inertial Measurement Unit (IMU), wheel encoder, and lidar odometry are utilized together to estimate the ego-motion of an unmanned ground vehicle. The IMU is fused with the wheel encoder to obtain the motion prior, and it is involved in three levels of the lidar odometry: Firstly, we use the IMU information to rectify the intra-frame distortion of the lidar scan, which is caused by the vehicle's own movement; secondly, the IMU provides a better initial guess for the lidar odometry; and thirdly, the IMU is fused with the lidar odometry in an Extended Kalman filter framework. In addition, an efficient method for hand–eye calibration between the IMU and the lidar is proposed. To evaluate the performance of our method, extensive experiments are performed and our system can output stable, accurate, and high-frequency localization results in diverse environment without any prior information. [ABSTRACT FROM AUTHOR]

Large-scale surfaces are prevalent in advanced manufacturing industries, and 3D profilometry of these surfaces plays a pivotal role for quality control. This paper proposes a novel and flexible large-scale 3D scanning system assembled by combining a robot, a binocular structured light scanner and a laser tracker. The measurement principle and system construction of the integrated system are introduced. A mathematical model is established for the global data fusion. Subsequently, a robust method is introduced for the establishment of the end coordinate system. As for hand-eye calibration, the calibration ball is observed by the scanner and the laser tracker simultaneously. With this data, the hand-eye relationship is solved, and then an algorithm is built to get the transformation matrix between the end coordinate system and the world coordinate system. A validation experiment is designed to verify the proposed algorithms. Firstly, a hand-eye calibration experiment is implemented and the computation of the transformation matrix is done. Then a car body rear is measured 22 times in order to verify the global data fusion algorithm. The 3D shape of the rear is reconstructed successfully. To evaluate the precision of the proposed method, a metric tool is built and the results are presented. [ABSTRACT FROM AUTHOR]

In order to improve industrial production efficiency, a hand–eye system based on 3D vision is proposed and the proposed system is applied to the assembly task of workpieces. First, a hand–eye calibration optimization algorithm based on data filtering is proposed in this paper. This method ensures the accuracy required for hand–eye calibration by filtering out part of the improper data. Furthermore, the improved U-net is adopted for image segmentation and SAC-IA coarse registration ICP fine registration method is adopted for point cloud registration. This method ensures that the 6D pose estimation of the object is more accurate. Through the hand–eye calibration method based on data filtering, the average error of hand–eye calibration is reduced by 0.42 mm to 0.08 mm. Compared with other models, the improved U-net proposed in this paper has higher accuracy for depth image segmentation, and the Acc coefficient and Dice coefficient achieve 0.961 and 0.876, respectively. The average translation error, average rotation error and average time-consuming of the object recognition and pose estimation methods proposed in this paper are 1.19 mm, 1.27°, and 7.5 s, respectively. The experimental results show that the proposed system in this paper can complete high-precision assembly tasks. [ABSTRACT FROM AUTHOR]

A robot can identify the position of a target and complete a grasping based on the hand–eye calibration algorithm, through which the relationship between the robot coordinate system and the camera coordinate system can be established. The accuracy of the hand–eye calibration algorithm affects the real-time performance of the visual servo system and the robot manipulation. The traditional calibration technique is based on a perfect mathematical model AX = XB, in which the X represents the relationship of (A) the camera coordinate system and (B) the robot coordinate system. The traditional solution to the transformation matrix has a certain extent of limitation and instability. To solve this problem, an optimized neural-network-based hand–eye calibration method was developed to establish a non-linear relationship between robotic coordinates and pixel coordinates that can compensate for the nonlinear distortion of the camera lens. The learning process of the hand–eye calibration model can be interpreted as B = f A , which is the coordinate transformation relationship trained by the neural network. An accurate hand–eye calibration model can finally be obtained by continuously optimizing the network structure and parameters via training. Finally, the accuracy and stability of the method were verified by experiments on a robot grasping system. [ABSTRACT FROM AUTHOR]

A method for simultaneous laser profilometer and hand–eye calibration in relation to an industrial robot as well as its implementation is presented. In contrast to other methods, the new calibration procedure requires the measurement of only one reference geometry to calculate all the transformation parameters. The reference geometry is measured with a laser profilometer from 15 different poses. The intrinsic parameters of the profilometer, as well as the extrinsic (hand–eye) parameters, are then numerically optimized to achieve the minimum deviation between the reference and the measured geometry. The method was characterized with experiments that revealed a standard deviation of the displacements between the reference geometry after the calibration of less than 0.105 mm in the case of using the robot-arm actuator and 0.046 mm in case of using a 5-axis CNC milling machine. The entire procedure, including measurement and calculation, can be completely automated and lasts less than 10 min. This opens up possibilities for regular on-site recalibration of the entire system. [ABSTRACT FROM AUTHOR]

The increasing capability of computing power and mobile graphics has made possible the release of self-contained augmented reality (AR) headsets featuring efficient head-anchored tracking solutions. Ego motion estimation based on well-established infrared tracking of markers ensures sufficient accuracy and robustness. Unfortunately, wearable visible-light stereo cameras with short baseline and operating under uncontrolled lighting conditions suffer from tracking failures and ambiguities in pose estimation. To improve the accuracy of optical self-tracking and its resiliency to marker occlusions, degraded camera calibrations, and inconsistent lighting, in this work we propose a sensor fusion approach based on Kalman filtering that integrates optical tracking data with inertial tracking data when computing motion correlation. In order to measure improvements in AR overlay accuracy, experiments are performed with a custom-made AR headset designed for supporting complex manual tasks performed under direct vision. Experimental results show that the proposed solution improves the head-mounted display (HMD) tracking accuracy by one third and improves the robustness by also capturing the orientation of the target scene when some of the markers are occluded and when the optical tracking yields unstable and/or ambiguous results due to the limitations of using head-anchored stereo tracking cameras under uncontrollable lighting conditions. [ABSTRACT FROM AUTHOR]

To improve the accuracy and robustness of hand–eye calibration, a hand–eye calibration method based on Schur matric decomposition is proposed in this paper. The accuracy of these methods strongly depends on the quality of observation data. Therefore, preprocessing observation data is essential. As with traditional two-step hand–eye calibration methods, we first solve the rotation parameters and then the translation vector can be immediately determined. A general solution was obtained from one observation through Schur matric decomposition and then the degrees of freedom were decreased from three to two. Observation data preprocessing is one of the basic unresolved problems with hand–eye calibration methods. A discriminant equation to delete outliers was deduced based on Schur matric decomposition. Finally, the basic problem of observation data preprocessing was solved using outlier detection, which significantly improved robustness. The proposed method was validated by both simulations and experiments. The results show that the prediction error of rotation and translation was 0.06 arcmin and 1.01 mm respectively, and the proposed method performed much better in outlier detection. A minimal configuration for the unique solution was proven from a new perspective. [ABSTRACT FROM AUTHOR]

In this paper, we propose two novel methods for robot-world-hand–eye calibration and provide a comparative analysis against six state-of-the-art methods. We examine the calibration problem from two alternative geometrical interpretations, called 'hand–eye' and 'robot-world-hand–eye', respectively. The study analyses the effects of specifying the objective function as pose error or reprojection error minimization problem. We provide three real and three simulated datasets with rendered images as part of the study. In addition, we propose a robotic arm error modeling approach to be used along with the simulated datasets for generating a realistic response. The tests on simulated data are performed in both ideal cases and with pseudo-realistic robotic arm pose and visual noise. Our methods show significant improvement and robustness on many metrics in various scenarios compared to state-of-the-art methods. [ABSTRACT FROM AUTHOR]

An extended robot–world and hand–eye calibration method is proposed in this paper to evaluate the transformation relationship between the camera and robot device. This approach could be performed for mobile or medical robotics applications, where precise, expensive, or unsterile calibration objects, or enough movement space, cannot be made available at the work site. Firstly, a mathematical model is established to formulate the robot-gripper-to-camera rigid transformation and robot-base-to-world rigid transformation using the Kronecker product. Subsequently, a sparse bundle adjustment is introduced for the optimization of robot–world and hand–eye calibration, as well as reconstruction results. Finally, a validation experiment including two kinds of real data sets is designed to demonstrate the effectiveness and accuracy of the proposed approach. The translation relative error of rigid transformation is less than 8/10,000 by a Denso robot in a movement range of 1.3 m × 1.3 m × 1.2 m. The distance measurement mean error after three-dimensional reconstruction is 0.13 mm. [ABSTRACT FROM AUTHOR]

Large-scale surfaces are prevalent in advanced manufacturing industries, and 3D profilometry of these surfaces plays a pivotal role for quality control. This paper proposes a novel and flexible large-scale 3D scanning system assembled by combining a robot, a binocular structured light scanner and a laser tracker. The measurement principle and system construction of the integrated system are introduced. A mathematical model is established for the global data fusion. Subsequently, a robust method is introduced for the establishment of the end coordinate system. As for hand-eye calibration, the calibration ball is observed by the scanner and the laser tracker simultaneously. With this data, the hand-eye relationship is solved, and then an algorithm is built to get the transformation matrix between the end coordinate system and the world coordinate system. A validation experiment is designed to verify the proposed algorithms. Firstly, a hand-eye calibration experiment is implemented and the computation of the transformation matrix is done. Then a car body rear is measured 22 times in order to verify the global data fusion algorithm. The 3D shape of the rear is reconstructed successfully. To evaluate the precision of the proposed method, a metric tool is built and the results are presented., Competing Interests: The authors declare no conflict of interest.