Line Structured Light Sensor Research Articles

Automated line scan profilometer based on the surface recognition method

Existing high-precision measurement devices face numerous challenges when dealing with complex surfaces, including high manual involvement, low measurement efficiency, and high costs. It is even more difficult to obtain complete surface data, particularly when confronted with unknown surfaces. This paper combines a fringe projection sensor with a high-precision line-structured light sensor to construct a line scan profiler. It proposes a surface recognition measurement method to overcome these challenges. This method fully leverages the fringe projection sensor's wide-field advantage to identify the object's surface shape and pose quickly. Subsequently, through the point cloud extraction and path planning algorithms proposed in this paper, efficient and complete line scan measurement paths are automatically generated, thus achieving the automation of the measurement process. Experimental measurements of aspherical lenses, freeform reflective mirrors, and artificial knee joint models validate the advantages of this method over existing methods in terms of measurement efficiency, automation capability, and applicability.

Error correction method based on dual-beam laser for curved rail profile measurement

The current method of dynamic rail profile measurement involves the installation of a line-structured light sensor at the base of the train. The accuracy of this measurement is influenced by the vertical relationship between the laser plane of the light sensor and the longitudinal direction of the rail (LDR). When a train travels in a straight line, the normal of the laser plane aligns with the LDR. However, when the train curves, the angle at which its wheels connect with the rails causes the laser plane’s normal direction to deviate from the LDR, leading to measurement errors. To address this issue, we propose a method for curved rail profile measurement using a dual-beam laser to correct these errors. This method involves generating an auxiliary 3D rail reflecting the LDR and a virtual 3D rail reflecting the normal direction of the laser plane from the cross-section image of the dual-beam laser. An optimization function is then formulated to determine the optimal auxiliary plane (optimal-AP) by analyzing the alignment or intersection between the auxiliary and virtual 3D rails. Distorted contour points are projected onto the optimal-AP to rectify errors. Experiments validate the accuracy and effectiveness of this proposed method. The results show that, regardless of pitch or yaw movement between the laser plane and the LDR, the error in measuring corrected profile wear remains consistently below 0.10 millimeters, thereby meeting the accuracy standard for rail wear measurement. This approach rectifies measurement errors in curved rail profiles from a 3D perspective, ensuring accurate measurements even under complex working conditions. It also provides a valuable reference for error analysis and improving dynamic rail profile measurement accuracy.

Hand-eye calibration of line structured-light sensor based on double-sphere constraints

Aiming at the complex problem of solving the positional relationship between the line laser sensor at the end of the robot wrist and the coordinate system of the end flange, a line laser hand-eye calibration method based on a double ball fixed target is proposed. Firstly, a double standard ball with known radius and center distance is used as a reference tool, and the point cloud data of a single contour line is collected several times, and the coordinates of the two ball centers under the camera coordinate system can be obtained according to the center coordinates of the circle obtained from the fitted circle combined with the Pythagoras theorem. Then, according to the principle that the two spherical centers are fixed under the robot base coordinate system, the rigid transformation matrix between the line laser sensor and the robot is successively solved. When solving the rotation matrix, the constraint of the distance between the spherical centers of the two balls is introduced to obtain a solution with better accuracy. Experimental results show that the method has better overall performance compared with the traditional single-ball hand-eye calibration method.

Hand-eye calibration of line structured-light sensor by scanning and reconstruction of a free-placed standard cylindrical target

This paper proposed a hand-eye calibration method for a line structured-light (LSL) scanner mounted on industrial robot. Unlike widely used spherical targets, the target used in our work is a cylinder, which is inexpensive and easy to be manufactured in larger size to cover a wider calibration space. In order to address the profile self-occlusion and drastically shape changing of conic section during calibration, an unconstrained dimensionality reduction method is proposed to fit the scanned profiles. Based on fitted ellipse centers under various robot poses, the hand-eye calibration model is developed based on the difference between cylinder radius and the distance from the profile point to the cylinder axis. Simulation and actual experiments have shown that the proposed ellipse fitting method is more accurate and robust, and the calibration method achieves higher reconstruction accuracy under the same robot positioning error, need less robot operations to obtain more accurate hand-eye parameters.

Simultaneous calibration of hand-eye and kinematics for industrial robot using line-structured light sensor

For visually guided industrial robots, it is necessary to calibrate the robot's hand-eye relationship to accurately guide the robot's operations with the measurement data of the visual coordinate system. In most cases, the calibration process of hand-eye error does not consider the impact of kinematic errors, which accumulates the error in the calibration process and reduces the calibration accuracy. To enhance the calibration accuracy of industrial robots, a simultaneous calibration method of robot hand-eye relationship and kinematics using a line-structured light sensor is proposed. A simultaneous calibration model is established by using the differential kinematics principle. Afterward, the line-structured light sensor and two standard balls with known center distances are used in the calibration and accuracy verification experiments. After calibration and compensation with the proposed method, the average distance error of the robot declined from 3.967 mm to 1.001 mm. The proposed simultaneous calibration method for hand-eye relationship and kinematics dramatically improves the robot's positioning accuracy.

Research on Welding Guidance System of Intelligent Perception for Steel Weldment

In order to achieve intelligent and flexible welding, complete the extraction and path planning of 3-D complex weld seam of large steel weldment, and solve the problems that in the vision-guided welding process, the scanning path depends on manual teaching, and the offline programming depends on the machining and assembly accuracy of the weldment; a vision-guided method for welding robots based on a line structured-light sensor is proposed. First, the 3-D computer aided design (CAD) model of the weldment is used to build an offline welding knowledge library, and then, the scanning path is planned in combination with the point cloud alignment. Then, the sensor collects the point cloud on the surface of the weldment along the scanning path. Second, different weld seam extraction algorithms are proposed according to different weld types. The random sample consensus (RANSAC) algorithm is used to identify the position of weld feature points, and the three-frame method is used to solve the pose of weld feature points. Finally, the weld seam is generated to guide the welding robot to complete the welding operation. The experimental results show that the proposed method can be used for different types and dimensions of weldments and provides flexible welding operations with better versatility and higher intelligence.

Flexible Scanning Method by Integrating Laser Line Sensors with Articulated Arm Coordinate Measuring Machines

Measuring and reconstructing the shape of workpieces have been considered as a fundamental step in both reverse engineering and product quality control. Owing to increasing structural complexity of recent products, measurements from multiple directions are typically required in current scanning techniques. Specifically, the plane structured light can be applied to measure one area of a part at a time, with an additional algorithm required to merge the collected data of each area. Alternatively, the line structured light sensor integrated on CNC machines or CMMs could also realize multi-view measurement. However, the system needs to be repeatedly calibrated at each new direction. This paper presents a flexible scanning method by integrating laser line sensors with articulated arm coordinate measuring machines (AACMM). Since the output of the laser line sensor is 2D raw data in the laser plane, our system model introduces an explicit transformation from the 2D sensor coordinate frame to the 3D base coordinate frame of the AACMM (i.e., the translation and rotation the of the 2D sensor coordinate in the sixth coordinate system of AACMM). To solve the model, the “conjugate pairs” are proposed and identified by measuring a fixed point (e.g., a sphere center). Moreover, a search algorithm is adopted to find the optimal solution, which noticeably boosts the model accuracy. The experimental results show that the error of the system is about 0.2 mm, which is caused by the error of the AACMM, the sensor error and the calibration error. By measuring a complicated part, the proposed system is proved to be flexible and facilitate, with the ability to measure a part expediently from any necessary direction. Furthermore, the proposed calibration method can also be used for robot hand-eye relationship calibration.

Research on Measurement of Tooth Profile Parameters of Synchronous Belt Based on Point Cloud Data.

Accurately detecting the tooth profile parameters of the synchronous belt is crucial for the transmission’s load distribution and service life. However, the existing detection methods have low efficiency, are greatly affected by the manual experience, and cannot realize automatic detection. A measurement method based on point cloud data is proposed to solve this issue. The surface space points of the synchronous belt are acquired by a line-structured light sensor, and the raw point clouds are preprocessed to remove outliers and reduce the number of points. Then, the point clouds are divided into plane and arc regions, and different methods are used for fitting. Finally, the parameters of each tooth are calculated. The experimental results show that the method has high measurement accuracy and reliable stability and can replace the original detection method to realize automatic detection.

A method to reduce the systematic error of line-structured light sensors based on light plane correction

Line-structured light sensors are widely used in 3-dimentional (3D) reconstruction, however, there are few studies on the systematic error and the correction method. To further improve the measurement accuracy, this paper establishes the systematic error model of the line-structured light sensor, and the distribution function of the systematic error caused by the inaccurate light plane equation is derived. Then, the measurement method of the systematic error is proposed, the distance error and the orientation error of the light plane equation are corrected in two steps according to the distribution characteristics of the systematic error. The experimental results show that the systematic error of the sensor is reduced by at least 80% after correction. This method effectively reduces the systematic error of the line-structured light sensor and has good robustness.

Research on the global calibration of the multi-vision line structured light measurement system based on auxiliary camera

<p indent="0mm">To solve the problem of the absence of a common field of view between the cameras in the existing multi-vision line structured light measurement system, which leads to high requirements for calibration tools and complicated global-calibration process, a global-calibration method for the multi-vision line structured light measurement system based on an auxiliary camera is introduced to achieve a large-field-of-view measurement. First, an auxiliary camera is used as an intermediary to acquire an approximately one-half overlapped field of view with the line structured light sensor, which can constitute a binocular subsystem. Then, the spatial geometric relationship between the auxiliary camera and two adjacent line structured light sensors is established based on the different pose images of the calibration plate. Second, the position relationship between the two adjacent line structured light sensors without a common field of view is solved using coordinate transformation. Finally, the transformation matrix from a local to a global coordinate system is solved one at a time to complete the global calibration process. The requirement in terms of calibration tools of the proposed calibration method is low. Further, it does require the use of high-precision measuring equipment and complex targets but only requires a simple chessboard target to complete the global calibration of a multi-vision line structured light measurement system. Meanwhile, the calibration process can achieve high calibration accuracy. Meanwhile, the measurement experiment demonstrates that the global calibration error of this method is <sc>0.067 mm,</sc> which effectively improves the measurement accuracy and can guarantee the required accuracy for the integration of “measurement and machining” of large components using robots.

An Effective Coaxiality Measurement for Twist Drill Based on Line Structured Light Sensor

Aiming at the accurate and effective coaxiality measurement of twist drills with irregular surface, an optical measurement mechanism is proposed in this paper. First, A high-precision rotation instrument based on four core units is designed, which can obtain 3D point cloud data of twist drills at all angles. Second, in the data processing stage, an improved robust Gaussian Mixture Model is proposed for accurate and rapid blade back segmentation. To improve the measurement efficiency, a rapid reconstruction method of twist drill axis based on orthogonal synthesis is proposed, which can rapidly locate the maximum deviation between the actual axis and the reference by using the extracted blade back data. Finally, by calculating the maximum radial Euclidean distance from the benchmark, the coaxiality error of the twist drill is obtained. Comparing with other measurement methods, the experimental results show that our proposed method performs well with high efficiency of less than 3 s/pc, and the average measurement error is about 0.020 mm. The experimental results and uncertainty analysis show that the proposed method is effective and automatic, and can be applied to the coaxiality measurement of twist drills of various specifications.

Data-Driven Laser Plane Optimization for High-Precision Numerical Calibration of Line Structured Light Sensors

Line structured light sensor has been applied in various three dimensional (3D) measurement scenes with the advantages of non-contact, low cost and high speed. Its accuracy, which is directly determined by the calibration method, needs to be further improved to fulfill the measuring tasks of precision parts. Here, we proposed a numerical method that can eliminate the errors of the model based methods through two strategies. One is the establishment of the numerical mapping relationship between stripe pixel coordinates and their world coordinates through piecewise cubic interpolation. Corner points of a checkerboard target are used to obtain sufficient interpolating nodes. This target can be manually aligned with the laser plane and the alignment error would be eliminated via the point projection. The other is the data-driven laser plane optimization. The data set of reference interval distance is computed based on the invariance of cross ratio. The optimization model is to minimize the root mean squared error of measured interval distances by adjusting the laser plane coefficients. After the optimization, a higher numerical mapping relationship can be achieved. It bypasses the camera and the distortion models and reaches a calibration error of only 0.005mm. The comparison studies and the measurement of the steps further validate the proposed method.

Real-Time Stripe Width Computation Using Back Propagation Neural Network for Adaptive Control of Line Structured Light Sensors.

A line structured light sensor (LSLS) is generally constituted of a laser line projector and a camera. With the advantages of simple construction, non-contact, and high measuring speed, it is of great perspective in 3D measurement. For traditional LSLSs, the camera exposure time is usually fixed while the surface properties can be varied for different measurement tasks. This would lead to under/over exposure of the stripe images or even failure of the measurement. To avoid these undesired situations, an adaptive control method was proposed to modulate the average stripe width (ASW) within a favorite range. The ASW is first computed based on the back propagation neural network (BPNN), which can reach a high accuracy result and reduce the runtime dramatically. Then, the approximate linear relationship between the ASW and the exposure time was demonstrated via a series of experiments. Thus, a linear iteration procedure was proposed to compute the optimal camera exposure time. When the optimized exposure time is real-time adjusted, stripe images with the favorite ASW can be obtained during the whole scanning process. The smoothness of the stripe center lines and the surface integrity can be improved. A small proportion of the invalid stripe images further proves the effectiveness of the control method.

Calibration Method of Roof Weld Coating Robot System Based on Plane-to-Plane Intersection Model

In order to solve the problems of traditional calibration methods, such as assigning internal and external parameters for camera and hand-eye relation matrices, a new method to calibrate weld coating robots based on plane-to-plane intersection model is developed in this paper. The mathematical model of the system is established by the homogeneous transformation theory, and then used to calibrate the line structured light sensor (LSLS). By adjusting the robot, the coordinate systems of the LSLS and the workpiece are superposed to solve the hand-eye relation matrix of the car roof weld coating robot. Finally, the calibrated experimental process and results towards car roof weld coating robot system are analyzed. The results show that this method is simple and high accuracy in roof weld coating robot system, and can be applied to the field calibration of car roof weld coating robot system. Furthermore, it can provide guiding significance for calibration of other robot systems.

一种基于线结构光传感器的圆位姿测量方法

一种基于线结构光传感器的圆位姿测量方法

3D measurement of gears based on a line structured light sensor

Gears are core components of transmission systems. The current trend of gear measurements is the rapid and full digitization. To rapidly obtain information on the three-dimensional (3D) shape of the gear tooth flank, a 3D point cloud measurement system based on a line structure light sensor and high precision air floating rotary table is proposed. The system can acquire more than 1.5 million 3D data points on one side of the gear tooth flank within less than 5 s. In this study, the measured 3D point cloud data is used to calculate the profile error, pitch error, then compared to those obtained from traditional contact measurements. In addition, tooth flank error was evaluated. The results show that the 3D point cloud measurement system can perform fast and accurately in 3D measurements of gears and it is a novel system for gear measurement and error calculation.

A Novel 3D Seam Extraction Method Based on Multi-Functional Sensor for V-Type Weld Seam

To meet the current flexible modern manufacturing mode, based on the idea from coarse to fine, a novel three-dimensional(3D) seam extraction algorithm is proposed in this paper which includes the global rough extraction and the local fine positioning. Firstly, a multi-functional vision system based on Digital Light Processing(DLP) projector is designed to adapt different 3D measurement tasks. Secondly, a global sensor based on DLP projector is constructed which is used to acquire the whole weld seam. Meanwhile, a new feature extraction algorithm based on 3D information of work pieces is proposed. Thirdly, a line structured light sensor based on DLP projector is to serve as local sensor. Based on the results of rough extraction, the close-range laser scanning is to improve the measurement precision. Finally, the 3D path model and pose estimation of weld seam are done which could be applied into the 3D path teaching of welding robots. The experiments show that the proposed algorithm could well finish the high-precision 3D seam path planning through the “Global-Local” method.

基于参考靶标的线结构光传感器标定

基于参考靶标的线结构光传感器标定

超大尺度线结构光传感器内外参数同时标定

超大尺度线结构光传感器内外参数同时标定

基于光条信度评价的线结构光传感器曝光时间优化

基于光条信度评价的线结构光传感器曝光时间优化