Motion-induced error reduction for phase-shifting profilometry with phase probability equalization

Wang, Yuwei; Cai, Jiaxu; Liu, Yuankun; Chen, Xiangcheng; Wang, Yajun

doi:10.1016/j.optlaseng.2022.107088

Cited by 20 publications

(9 citation statements)

References 43 publications

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“…Tao et al 16 proposed an adaptive depth constraint method, and Wang et al 17 proposed a Hilbert transform method, which can eliminate motion-induced errors to a certain extent, but they have limitations on the slope of the object itself. Wang et al 18 used a phase equalization method to remove motion-induced errors, but this method cannot be applied to objects with uneven brightness. Therefore, it is crucial to develop a 3D measurement method that has high applicability and can effectively eliminate motion-induced errors.…”

Section: Introductionmentioning

confidence: 99%

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Method for measuring the three-dimensional profile of moving objects based on neural network position transformation

Wu,

Di,

Guo

et al. 2024

Opt. Eng.

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To eliminate motion-induced errors, a multi-step fitting position transformation method is proposed for measuring the three-dimensional (3D) profile of an object. The method employs a neural network to perform position transformation on the objects in the captured motion phase-shifting fringe projection images, aligning them to the same position, and then uses the phase-shifting method to obtain the 3D profile of the objects. Theoretically, the rigid body motion transformation matrix is used to deduce the change of points on the objects in the fringe pattern under different motion conditions, and the 3D profile measurement results of randomly moving objects are simulated and analyzed. Experimentally, a neural network model was trained by using 5000 sets of data obtained from experiments to eliminate motioninduced errors in various motion states. Simulation and experimental results show that this method significantly reduces the motion-induced errors that occur when measuring moving objects using the traditional phase-shifting method. It has broad application prospects in the field of real-time measurement of moving objects' 3D profiles.

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Section: Introductionmentioning

confidence: 99%

“…proposed a Hilbert transform method, which can eliminate motion-induced errors to a certain extent, but they have limitations on the slope of the object itself. Wang et al 18 . used a phase equalization method to remove motion-induced errors, but this method cannot be applied to objects with uneven brightness.…”

Section: Introductionmentioning

confidence: 99%

Method for measuring the three-dimensional profile of moving objects based on neural network position transformation

Wu,

Di,

Guo

et al. 2024

Opt. Eng.

View full text Add to dashboard Cite

show abstract

“…Therefore, scholars have conducted corresponding research on phase-shifting profilometry (PSP) to solve these problems. [14][15][16][17][18] For PSP technology, Chen et al 19 put forward the concept of phase correction and studied motion compensation under the condition of translation and tilt. This method can reduce phase errors, and its feasibility of reducing the measurement error was proven through phase correction in a phase shift method.…”

Section: Introductionmentioning

confidence: 99%

Research on error compensation method of topography dynamic reconstruction of chattering thin-walled parts based on structured light detection

Li,

Lv,

Liu

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part C:

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In order to achieve visual measurement error compensation for the surface of thin-walled components with chatter, a new real-time adaptive correction method for chatter motion error is proposed in this paper. Firstly, according to the mechanism of the chatter effect, an error analysis model of the dynamic structured light phase principal value is derived. Then, a real-time optimization method for the motion phase error in the frame of structured light projection is proposed. Meanwhile, a compensation method based on an adaptive grating projection period is proposed to eliminate the cumulative error of inter-frame chatter motion. Finally, a structured light visual inspection system and an experimental platform for chattering thin-walled parts are established for experimental verification. The experimental results under slight and severe chatter conditions indicate that the proposed method can optimize the phase errors within the projected frame in real time under any scenario, compensate for the cumulative errors between projected frames, and improve the point cloud accuracy of real-time 3D reconstruction.

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“…Since LiDAR (laser imaging, detection and ranging) can obtain accurate point cloud data of objects according to the echo detection principle (Reiser et al, 2018;Wang et al, 2018;Jafari Malekabadi et al, 2019) and is less affected by light (Wang et al, 2022a;Wang et al, 2022b), it can supplement the missing information caused by the use of machine vision (Jeong et al, 2018;Aguiar et al, 2021). In order to solve the issue of information loss when a vision sensor was used to obtain information, a method using LiDAR supplement vision was proposed (Bae et al, 2021), which pooled the strength of each sensor and made up for the shortcomings of using a single sensor.…”

Section: Introductionmentioning

confidence: 99%

Inter-row information recognition of maize in the middle and late stages via LiDAR supplementary vision

Xie

Liu

et al. 2022

Front. Plant Sci.

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In the middle and late stages of maize, light is limited and non-maize obstacles exist. When a plant protection robot uses the traditional visual navigation method to obtain navigation information, some information will be missing. Therefore, this paper proposed a method using LiDAR (laser imaging, detection and ranging) point cloud data to supplement machine vision data for recognizing inter-row information in the middle and late stages of maize. Firstly, we improved the YOLOv5 (You Only Look Once, version 5) algorithm based on the characteristics of the actual maize inter-row environment in the middle and late stages by introducing MobileNetv2 and ECANet. Compared with that of YOLOv5, the frame rate of the improved YOLOv5 (Im-YOLOv5) increased by 17.91% and the weight size decreased by 55.56% when the average accuracy was reduced by only 0.35%, improving the detection performance and shortening the time of model reasoning. Secondly, we identified obstacles (such as stones and clods) between the rows using the LiDAR point cloud data to obtain auxiliary navigation information. Thirdly, the auxiliary navigation information was used to supplement the visual information, so that not only the recognition accuracy of the inter-row navigation information in the middle and late stages of maize was improved but also the basis of the stable and efficient operation of the inter-row plant protection robot was provided for these stages. The experimental results from a data acquisition robot equipped with a camera and a LiDAR sensor are presented to show the efficacy and remarkable performance of the proposed method.

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Motion-induced error reduction for phase-shifting profilometry with phase probability equalization

Cited by 20 publications

References 43 publications

Method for measuring the three-dimensional profile of moving objects based on neural network position transformation

Method for measuring the three-dimensional profile of moving objects based on neural network position transformation

Research on error compensation method of topography dynamic reconstruction of chattering thin-walled parts based on structured light detection

Inter-row information recognition of maize in the middle and late stages via LiDAR supplementary vision

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