Visual Measurement and Calibration of Geometric Errors of Rotating Axes for Five-axis Platform

In this paper, a method for identifying and decoupling geometric errors of rotation axes using vision measurement is proposed. Based on screw theory and exponential product formula, identification equations of position-dependent geometric errors (PDGEs) and position-independent geometric errors (PIGEs) of the rotation axes are established. The mapping relationships between the error twist and geometric errors are established. The error model provides the coupling mechanism of PDGEs and PIGEs. Furthermore, a progressive decoupling method is proposed to separate PDGEs and PIGEs without additional assumptions. The pose parameters, required for solving the identification equations, are obtained by visual measurement. Then, the error terms of PIGEs and PDGEs are determined. Lastly, the error calibration of the rotation axes is investigated, thus providing an average rotary table orientation error reduction of 28.1% compared to the situation before calibration.

An improved posture evaluation method of cylindrical intersecting holes on large aerospace component based on monocular vision

Cylindrical intersecting holes(CIHs) are common connection and location reference features in assembly of large aerospace structures such as missile and rocket cabins. The posture accuracy of assembly holes significantly impacts the relative position accuracy of joined parts and fatigue strength of finished product. At present, monocular vision measurement is widely used in automatic drilling of assembly holes for its integration simplicity and lower cost, but in most research, only the front face edge of the hole is used in the measurement model, and the hole end surface is usually assumed to be plane, which inevitably leads to precision loss. In this research, a novel posture measurement method for CIHs is proposed. Firstly, by introducing an ambiguity removal strategy, a coarse posture estimation method based on plane hypothesis of the two end surfaces of CIHs is suggested. Secondly, considering that there is no simply explicit expression for CIHs edge, thus it is difficult to adopt the conventional model projection based pose optimization method. In view of this, the three-dimensional points corresponding to the edge pixels of CIHs image are derived, and the pose optimization model is established by minimizing the deviations between the distance from the points to the CIHs axis and the hole radius. Moreover, to better control the direction parameters of CIHs during the global optimization process, the approximately perpendicular and intersection constraints between CIHs axis and cylindrical component axis are involved in solution. The effectiveness of the posture measurement method is verified by comparative experiments with current methods and CMM, which demonstrates improvements on both measurement accuracy and robustness.

An Improved Initialization Method for Monocular Visual-Inertial SLAM

In the aim of improving the positioning accuracy of the monocular visual-inertial simultaneous localization and mapping (VI-SLAM) system, an improved initialization method with faster convergence is proposed. This approach is classified into three parts: Firstly, in the initial stage, the pure vision measurement model of ORB-SLAM is employed to make all the variables visible. Secondly, the frequency of the IMU and camera was aligned by IMU pre-integration technology. Thirdly, an improved iterative method is put forward for estimating the initial parameters of IMU faster. The estimation of IMU initial parameters is divided into several simpler sub-problems, containing direction refinement gravity estimation, gyroscope deviation estimation, accelerometer bias, and scale estimation. The experimental results on the self-built robot platform show that our method can up-regulate the initialization convergence speed, simultaneously improve the positioning accuracy of the entire VI-SLAM system.

Prediction of Surface Roughness in CNC Turning Process using Adaptive Neural Fuzzy Inference System

This paper presents the methodology of surface roughness inspection in the CNC Turning process. Adaptive Neural Fuzzy Inference System classifier can predict the high accuracy roughness value by insisting on surface roughness image. The vision system captures the image and determines the mean value by using the ANFIS algorithm. Training sets variables speed, depth of cut, feed rate, and mean value are feed as the input, and manual stylus probe surface roughness value is feed as the output. After the simulation process, the testing input was performed, and finally getting the vision measurement value. This higher accuracy (above 95%) and low error rate (below 4%) can be achieved by using the ANFIS classifier, which is predominantly helpful for the industry to measure surface roughness. Assign the quality of the product by evaluating the manual stylus probe and vision measurement value.

The Application of Visual Sensors in the Artistic Design of College Campus Landscape

The research purpose is to solve the problems of low efficiency, low accuracy, and high cost of traditional environmental landscape mapping and landscape volume measurement methods in the artistic design of college campus landscape and make up the defects that the traditional campus monitoring is vulnerable to adverse weather, which results in low monitoring accuracy. Primarily, a binocular stereo vision measurement based on Scale Invariant Feature Transform (SIFT) matching algorithm is proposed, which can realize accurate collection of environmental spatial information and measurement of landscape volume without contact in the process of campus landscape design. Then, the visual monitoring system of college landscape based on the Internet of Things (IoT) is constructed to realize real-time monitoring and early warning of human damage to campus landscape. The proposed method is verified by actual measurement of different objects and simulation experiments using simulation software. Ultimately, the application of visual sensors in artistic design of college campus landscape is analysed by literature analysis. The results show that (1) the error of the improved binocular stereo vision measurement designed here is 52.32% and 59.69% lower than that of the traditional measurement method when measuring the same object with different volumes and the volumes of different objects, respectively, which indicates that the measurement accuracy of the new method is higher. (2) The proposed landscape visual monitoring method based on IoT improves the image recognition accuracy by 21% compared with the traditional digital image monitoring method. The average recognition time is shortened by 12 ms, which ensures the accuracy and improves the recognition efficiency. (3) Through the analysis of existing literature, it is found that the binocular stereo vision sensor can be used to monitor the whole process of landscape construction in real time. The sensor can be combined with social networks, mobile terminals, and physiological monitoring equipment to comprehensively analyse and evaluate people’s preference for campus landscape. The proposed method has broad application prospects in campus landscape design, construction, and maintenance. The research purpose is to provide important technical support for the improvement of the overall image of the college campus and even the city for the design of landscape environment and the technical upgrading of maintenance work in the college campus.

An Improved Initialization Method for Monocular Visual-Inertial SLAM

In the aim of improving the positioning accuracy of monocular visual inertial simultaneous localization and mapping (VI-SLAM) system, an improved initialization method with faster convergence is proposed. This approach is classified as three parts: Firstly, in the initial stage, the pure vision measurement model of ORB-SLAM is employed to make all the variables visible. Secondly, the frequency of IMU camera was aligned by IMU preintegration technology. Thirdly, an improved iterative method is put forward for estimating the initial parameters of IMU faster. The estimation of IMU initial parameters is divided into several simpler sub-problems, containing direction refinement gravity estimation, gyroscope deviation estimation, accelerometer bias and scale estimation. The experimental results on the self-built robot platform show that our method can up-regulate the initialization convergence speed, simultaneously improve the positioning accuracy of the entire VI-SLAM system.

Transmission corridor line safety monitoring method based on monocular vision

Aiming at the problem of low accuracy of traditional methods in transmission corridor line safety monitoring, a transmission corridor line safety monitoring method based on monocular vision is designed. First of all, this article defines the target object of the safety monitoring of the transmission corridor line. Then, based on the monocular vision measurement to obtain the internal parameters of the camera geometry, optical characteristics, etc., the position of the power transmission corridor line security surveillance camera is calibrated, the power transmission corridor line security monitoring graphics are displayed, and the security monitoring data of the transmission corridor line is used for perception and track.Finally, the safety monitoring and early warning of transmission corridor lines are realized. The experimental results show that the monitoring accuracy of the design method is significantly higher than that of the control group, which can solve the problem of low accuracy of transmission corridor line safety monitoring by traditional methods.