Lever Arm Compensation of Autonomous Underwater Vehicle for Fast Transfer Alignment

Wang, Qi; Yang, Chao; Wu, Shaoen; Wang, Yuxiang

doi:10.32604/cmc.2019.03739

Cited by 10 publications

(6 citation statements)

References 13 publications

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“…There are different kinds of information transmitted between vehicles in VANETs, and many scholars have proposed information transmission protocols between vehicles [29][30][31]. Korkmaz et al [32] proposed a new and efficient multi-hop broadcast protocol (UMB) based on IEEE 802.11, aiming at solving the problems of the broadcast storm, hidden nodes and reliability of multi-hop broadcast in cities.…”

Section: Related Workmentioning

confidence: 99%

Exploring the Influence of Vehicle Mobility on Information Spreading in VANETs

2021

KSII TIIS

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With the advent of 5G communications, internet of vehicles technology has been widely used in vehicles. Then the dynamic spread of information between vehicles began to come into focus with more research. It is well known that the identification of nodes with great spread influence has always been a hot topic in the field of information spreading. Most of the existing work measures the propagation influence by degree centrality, betweenness centrality and closeness centrality. In this paper, we will identify influential vehicle nodes based on the mobility characteristics of vehicles to explore the information spreading between vehicles in VANETs. Different from the above methods, we mainly explore the influence of the radius of gyration and vehicle kilometers of travel on information spreading. We use a real vehicle trajectory data to simulate the information transmission process between vehicles based on the susceptible-infected-recovered SIR model. The experimental results show that the influence of information spreading does not enhance with increasing radius of gyration and vehicle kilometers of travel. The fact is that both the radius of gyration and the distance travelled have a significant influence on information spreading when they are close to the median. When the value of both is large or small, it has little influence on information spreading. In view of this results, we can use the radius of gyration and vehicle kilometers of travel to better facilitate the transmission of information between vehicles.

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Section: Related Workmentioning

confidence: 99%

Exploring the Influence of Vehicle Mobility on Information Spreading in VANETs

2021

KSII TIIS

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“…Structured light technology projects a sequence of fringe images on the surface of the measurement object, and the fringe is deformed by the contour of the object. The phase calculation [5][6][7] of the collected fringe image can realize reconstruction of the three-dimensional contour of the object. However, due to the influence of instrument design, gamma nonlinear distortion [8] between the projector and camera will produce measurement phase errors, and the collected grating fringes will not be an ideal cosine function, but a function with certain distortion.…”

Section: Prefacementioning

confidence: 99%

Phase Error Compensation of Three-Dimensional Reconstruction Combined with Hilbert Transform

Zhang¹,

Shen²,

Wu³

2021

Computers, Materials &Amp; Continua

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Nonlinear response is an important factor affecting the accuracy of three-dimensional image measurement based on the fringe structured light method. A phase compensation algorithm combined with a Hilbert transform is proposed to reduce the phase error caused by the nonlinear response of a digital projector in the three-dimensional measurement system of fringe structured light. According to the analysis of the influence of Gamma distortion on the phase calculation, the algorithm establishes the relationship model between phase error and harmonic coefficient, introduces phase shift to the signal, and keeps the signal amplitude constant while filtering out the DC component. The phase error is converted to the transform domain, and compared with the numeric value in the space domain. The algorithm is combined with a spiral phase function to optimize the Hilbert transform, so as to eliminate external noise, enhance the image quality, and get an accurate phase value. Experimental results show that the proposed method can effectively improve the accuracy and speed of phase measurement. By performing phase error compensation for free-form surface objects, the phase error is reduced by about 26%, and about 27% of the image reconstruction time is saved, which further demonstrates the feasibility and effectiveness of the method.

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“…The structure of the image acquisition and processing system is: using industrial cameras to connect with the computer through the USB interface on the motherboard, and transmit the image data directly to the industrial computer image processing software, and process and recognise the image (Duan et al, 2016;Wang et al, 2019). The performance of industrial cameras has an important impact on the accuracy of machine vision, and the selection of the type of industrial cameras needs to consider the characteristic parameters needed in the working environment.…”

Section: Gps Navigation Positioning Modulementioning

confidence: 99%

Application of vision aided strapdown integrated navigation in lane vehicles

Wang

Yang

2020

IJES

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The application of global navigation satellite system (GNSS) is extensive in lane applications with the development of science and technology. Vision aided strapdown integrated navigation is an effective aided-navigation method in the case of GNSS failure in lane vehicles, which plays an important role in realising high-precision navigation of lane navigation system. A vision-aided navigation system based on GNSS positioning is constructed using the electrical powered platform as the research object. The hardware platform of vision navigation system is presented and digital image processing is used to segment the collected lane image. The image pre-processing operation, including denoising filtering and greyscale processing, is carried out to complete the segmentation and get the effective navigation area. According to the effective area of navigation, a navigation datum line is extracted by the least squares linear fitting and Hough transform. According to the camera imaging model and the camera's internal and external parameters, the navigation datum line in the image coordinates is transformed into the world coordinates, and the heading angle is calculated. Kalman filter algorithm is used to fuse the navigation parameters of the vision navigation module and the GNSS positioning module, and the integrated navigation model is established.

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Lever Arm Compensation of Autonomous Underwater Vehicle for Fast Transfer Alignment

Cited by 10 publications

References 13 publications

Exploring the Influence of Vehicle Mobility on Information Spreading in VANETs

Exploring the Influence of Vehicle Mobility on Information Spreading in VANETs

Phase Error Compensation of Three-Dimensional Reconstruction Combined with Hilbert Transform

Application of vision aided strapdown integrated navigation in lane vehicles

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