The self-rotating three-dimensional (3D) visual scanning system is a prevalent technique for 3D reconstruction. However, the misalignment of the scanner’s rotation axis with the camera’s origin often necessitates specialized calibration tools to establish their relative positions. We propose a rotating axis calibration method for the rotation axis, which eliminates the need for specific tools. A rotating mechanism is used to rotate the 3D camera to different angles and carry out 3D reconstruction of the same object with curved surface characteristics, respectively. The resulting sequence of 3D point clouds is registered to derive the camera’s position transformation matrix corresponding to each rotation angle. Subsequently, we compute the positional relationship between the camera and the rotation axis by solving the equation. To prevent excessively large rotation angles that would cause the object beyond the camera’s field of view, the camera field extension method is proposed, which enhances equation redundancy and leads to improved calibration accuracy. Experiments prove the flexibility and accuracy of the proposed method.
Cameras offer a unique capability of collecting high density spatial data from a distant scene of interest. They can be employed as remote monitoring or inspection sensors to measure vibrating objects because of their commonplace availability, simplicity, and potentially low cost. A defect of vibrating measurement with the camera is to process the massive data generated by camera. In order to reduce the data collected from the camera, the camera using electronic rolling shutter (ERS) is applied to measure the frequency of one-dimensional vibration, whose frequency is much higher than the speed of the camera. Every row in the image captured by the ERS camera records the vibrating displacement at different times. Those displacements that form the vibration could be extracted by local analysis with sliding windows. This methodology is demonstrated on vibrating structures, a cantilever beam, and an air compressor to identify the validity of the proposed algorithm. Suggestions for applications of this methodology and challenges in real-world implementation are given at last.
KEYWORDS: Data acquisition, Human-machine interfaces, LabVIEW, Sensors, Time metrology, Control systems, Computing systems, Signal processing, Kinematics, Signal detection
Motorcycle crankshaft is a special rigid rotor. It is composed of crankshaft, connecting rod and slider. It belongs to
unbalanced rotor. Most of traditional methods of measuring unbalance value of crankshaft are not continuous, which
need human intervention. So the measurement time is long and measurement accuracy is not high. To solve the above
problem, a novel computer-based measurement is developed. The software of the measuring system is developed based
on G-language, namely LabVIEW. The hardware system includes accelerate sensors, multi-function Data Acquisition
(DAQ) card and industrial control computer. When the crankshaft rotates, its centrifugal forces are generated which
result in the supporting structure (also called vibration table) vibrating. Data acquisition, signal processing and analysis
can obtain unbalance value including amplitude and phrases. Computer-based measurement is used with software to set
up automated test system that can make fast measurements without human intervention. The application of virtual
instruments makes date analysis more accurate, and decreases the measuring time significantly; a complete measurement
can be finished in 25s. The results show that this new measuring system has the advantages of easy-of-use, high
precision, high efficiency and low costs.
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