Analysis of Laser Interferometer Measurement Uncertainty by Simulating Error Sources

Lipus, Lucija C.; Budzyn, Grzegorz; Ačko, Bojan

doi:10.2507/ijsimm20-2-563

Cited by 4 publications

(1 citation statement)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At present, most high-precision, large-range displacement measurement systems are developed by using laser interferometry or traditional grating sensing. Laser interferometers have the highest accuracy, but they require precise alignment installation and very strict environmental control, which limits their application in industry [7,8]. Traditional grating displacement sensors include optical encoders, magnetic encoders, capacitive encoders and the inductosyn.…”

Section: Introductionmentioning

confidence: 99%

A New Image Grating Sensor for Linear Displacement Measurement and Its Error Analysis

Fang

Zhou²,

Yu³

et al. 2022

Sensors

View full text Add to dashboard Cite

To improve the accuracy of the current vision-based linear displacement measurement in a large range, a new type of linear displacement sensing system, namely, image grating, is proposed in this paper. The proposed system included a patterned glass plate attached to the moving object and an ultra-low distortion lens for high-accuracy image matching. A DFT local up-sampling phase correlation method was adopted to obtain the sub-pixel translation of the patterns onto the target plate. Multiple sets of stripe patterns with different designs were located on the glass plate to expand the measurement range, based on the principle of phase correlation. In order to improve the measurement accuracy, the main errors of the image grating system were analyzed, and the nonlinear error compensation was completed based on the dynamic calibration of the pixel equivalent. The measurement results, after the error compensation, showed that the total error of the proposed system was less than 2.5 μm in the range of 60 mm, and the repeatability was within 0.16 μm, as quantified by standard deviation.

show abstract

Section: Introductionmentioning

confidence: 99%

A New Image Grating Sensor for Linear Displacement Measurement and Its Error Analysis

Fang

Zhou²,

Yu³

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

Uncertainty assessment in the calibration of an auto-compensated laser interferometer system

Samoudi,

Bendaou

2024

J Opt

View full text Add to dashboard Cite

An Object Association Matching Method Based on V2I System

Jin,

Yan,

Jiang

2023

Int J Comput Intell Syst

View full text Add to dashboard Cite

Vehicle-to-infrastructure (V2I) is one of the effective ways to solve the problem of intelligent connected vehicle perception, and the core is to fuse the information sensed by vehicle sensors with that sensed by infrastructure sensors. However, accurately matching the objects detected by the vehicle with multiple objects detected by the infrastructure remains a challenge. This paper presents an object association matching method to fuse the object information from vehicle sensors and roadside sensors, enabling the matching and fusion of multiple target information. The proposed object association matching algorithm consists of three steps. First, the deployment method for vehicle sensors and roadside sensors is designed. Then, the laser point cloud data from the roadside sensors are processed using the DBSCAN algorithm to extract the object information on the road. Finally, an improved single-pass algorithm for object association matching is proposed to achieve the matched target by setting a change threshold for selection. To validate the effectiveness and feasibility of the proposed method, real-vehicle experiments are conducted. Furthermore, the improved single-pass algorithm is compared with the classical Hungarian algorithm, Kuhn–Munkres (KM) algorithm, and nearest neighbor (NN) algorithm. The experimental results demonstrate that the improved single-pass algorithm achieves a target trajectory matching accuracy of 0.937, which is 6.60%, 1.85%, and 2.07% higher than the above-mentioned algorithms, respectively. In addition, this paper investigates the curvature of the target vehicle trajectory data after fusing vehicle sensing information and roadside sensing information. The curvature mean, curvature variance, and curvature standard deviation are analyzed. The experimental results illustrate that the fused target information is more accurate and effective. The method proposed in this study contributes to the advancement of the theoretical system of V2I cooperative perception and provides theoretical support for the development of intelligent connected vehicles.

show abstract

Analysis of Laser Interferometer Measurement Uncertainty by Simulating Error Sources

Cited by 4 publications

References 18 publications

A New Image Grating Sensor for Linear Displacement Measurement and Its Error Analysis

A New Image Grating Sensor for Linear Displacement Measurement and Its Error Analysis

Uncertainty assessment in the calibration of an auto-compensated laser interferometer system

An Object Association Matching Method Based on V2I System

Contact Info

Product

Resources

About