A measurement system based on internal cooperation of cameras in binocular vision

Zhou, Yaqin; Li, Qingwu; Chu, Lulu; Ma, Yunpeng; Zhang, Jianqing

doi:10.1088/1361-6501/ab6ecd

Cited by 24 publications

(10 citation statements)

References 37 publications

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“…Compared to other techniques, visual localization technology has advantages such as simple structure, no accumulation error, and high cost-effectiveness. Zhou et al [20] proposed a measurement method that utilizes monocular and binocular visual synergy, resulting in absolute and relative measurement errors below 3.5%. Wu et al [21] proposed a three-dimensional spatial motion measurement method for a robotic end effector utilizing binocular vision.…”

Section: Introductionmentioning

confidence: 99%

Position measurement technology of boom-type roadheader based on binocular vision

Cheng,

Wang,

Zheng

et al. 2023

Meas. Sci. Technol.

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Positioning of underground mining equipment in coal mines is a primary challenge in the intelligent development of tunnel excavation. Accurate and reliable position measurement plays a crucial role in improving excavation efficiency. However, the stability and accuracy of traditional measurement methods are difficult to guarantee due to factors such as vibration, magnetic interference, and the absence of GPS signals in coal mine environments. To address the problem of unstable measurement of the boom-type roadheader‘s position, this paper proposes a binocular vision-based measurement technique that utilizes four light spots as characteristic points. By processing the target images captured by the binocular camera and combining the minimum bounding rectangle and ellipse fitting of the spot regions, the method successfully obtains the four light spot characteristics. Subsequently, precise matching and stereo distance measurement of the target in the left and right images enable the determination of the boom-type roadheader’s posi-tion information. A positioning platform based on binocular vision is built and experimentally evaluated. The results demonstrate that this method can achieve accurate spot extraction and stable measurement of the machine’s position, even in complex backgrounds such as mixed lighting and low illumination. The planar measurement errors within a distance range of 50 m are all within ±25 mm, which basically meets the required construction precision for tunnel excavation.

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Section: Introductionmentioning

confidence: 99%

Position measurement technology of boom-type roadheader based on binocular vision

Cheng,

Wang,

Zheng

et al. 2023

Meas. Sci. Technol.

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“…Plane dimensional measurement of large workpieces is realized, and accuracy is improved by 53% [15]. Zhou et al proposed a binocular vision measurement method based on the internal collaboration of cameras, which achieves accurate dimensional measurement of objects by accumulating the number of pixels, and the relative measurement error is less than 3.5% [16]. Chen et al proposed a workpiece positioning algorithm based on Zenik moment and angle matching, combined with epipolar-geometry and the invariance of cross-ratio (CMEC), and A high-precision measurement method based on binocular vision is designed to realize the measurement of the length and dimension of the workpiece [17].…”

Section: Introductionmentioning

confidence: 99%

Multi-angle 3D measurement of small workpieces using EDMS algorithm

Song

Zhang

et al. 2023

Meas. Sci. Technol.

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Binocular vision can get the three-dimensional information of the object based on the two-dimensional image. However, when the background texture information of the workpiece to be measured is weak, or the depth information cannot be recognized due to the change of viewing angle, it will lead to poor three-dimensional measurement accuracy. To address this problem, the paper proposes a multi-view workpiece 3D measurement method based on binocular vision. First, an experimental bench with a Chessboard is designed. Corner point reconstruction by extracting calibration plate corner points. And the least squares method is used to fit the plane of the checkerboard lattice to obtain the plane mathematical model of the checkerboard lattice. Then, the vertices of the workpiece are extracted at the subpixel level, A decentered minimum distance sparse vertex stereo matching algorithm (EDMS) based on the Euclidean distance metric is proposed to achieve accurate and fast matching of corner points. Finally, the three-dimensional dimensions of the workpiece are calculated and experiments are conducted for each of the two workpieces at multiple angles. The results show that the average absolute error measured by the method at different angles is 0.33mm, the total relative error is 0.90%, and the variance is less than that 0.01mm2, Achieves more accurate multi-view 3D measurement of small workpieces. And it provides a new method for handheld mobile 3D measurement equipment with binocular vision.

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“…Constrained by complex operating environments affected by various weather conditions, a single sensor solution is not capable of fully describing the environment information required for a safe and reliable operation of automatic electric shovels. To overcome these issues, multiple sensors, such as LiDAR, inertial navigation and RTK, can be implemented to work coherently to provide enriched data information [ 22 , 23 , 24 , 25 , 26 ].…”

Section: Introductionmentioning

confidence: 99%

A Multi-Sensor Environmental Perception System for an Automatic Electric Shovel Platform

Liu

et al. 2021

Sensors

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Electric shovels have been widely used in heavy industrial applications, such as mineral extraction. However, the performance of the electric shovel is often affected by the complicated working environment and the proficiency of the operator, which will affect safety and efficiency. To improve the extraction performance, it is particularly important to study an intelligent electric shovel with autonomous operation technology. An electric shovel experimental platform for intelligent technology research and testing is proposed in this paper. The core of the designed platform is an intelligent environmental sensing/perception system, in which multiple sensors, such as RTK (real-time kinematic), IMU (inertial measurement unit) and LiDAR (light detection and ranging), have been employed. By appreciating the multi-directional loading characteristics of electric shovels, two 2D-LiDARs have been used and their data are synchronized and fused to construct a 3D point cloud. The synchronization is achieved with the assistance of RTK and IMU, which provide pose information of the shovel. In addition, in order to down-sample the LiDAR point clouds to facilitate more efficient data analysis, a new point cloud data processing algorithm including a bilateral-filtering based noise filter and a grid-based data compression method is proposed. The designed platform, together with its sensing system, was tested in different outdoor environment conditions. Compared with the original LiDAR point cloud, the proposed new environment sensing/perception system not only guarantees the characteristic points and effective edges of the measured objects, but also reduces the amount of processing point cloud data and improves system efficiency. By undertaking a large number of experiments, the overall measurement error of the proposed system is within 50 mm, which is well beyond the requirements of electric shovel application. The environment perception system for the automatic electric shovel platform has great research value and engineering significance for the improvement of the service problem of the electric shovel.

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A measurement system based on internal cooperation of cameras in binocular vision

Cited by 24 publications

References 37 publications

Position measurement technology of boom-type roadheader based on binocular vision

Position measurement technology of boom-type roadheader based on binocular vision

Multi-angle 3D measurement of small workpieces using EDMS algorithm

A Multi-Sensor Environmental Perception System for an Automatic Electric Shovel Platform

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