| With the economic development,China’s road transportation industry is booming.As an indispensable means of transportation,trucks have brought convenience to people’s lives,but also brought problems such as traffic safety and environmental pollution.In recent years,the incidence of truck accidents remains high.In addition to factors such as driver fatigue,driving road conditions,and poor driving environment,inaccurate wheel alignment is one of the main factors causing frequent accidents.For trucks,due to their multiple axles,large load carrying capacity,and high driving speed,once there is a problem with wheel alignment,it can cause a series of problems such as abnormal tire wear,deviation,and shimmy,which are closely related to driving safety and cannot be ignored.Traditional wheel alignment devices for large vehicles have complex processes,difficult installation,inaccurate models,and poor accuracy.With the widespread application of machine vision in different fields,the rapid development of passenger vehicle wheel alignment technology has led to the emergence of quite a number of brands of 3D wheel alignment devices.However,wheel alignment devices for large trucks are still rare and expensive,so the research on detection methods for wheel alignment parameters of trucks is of great significance.Based on the 3D four-wheel alignment principle of small vehicles,combined with the characteristics of large trucks such as multiple axles and large dimensions,and based on machine vision,this paper proposes a wheel alignment parameter detection scheme for large vehicles.The main research contents of this paper include the following aspects:1.Establish a mathematical model for truck wheel alignment parametersThis paper introduces the concept and function of wheel alignment parameters for large trucks,constructs a measurement model for wheel alignment parameters for trucks,and provides different alignment parameters calculation methods for steering and non steering wheels,including steering wheel toe in,camber,caster,kingpin inclination,and axle deviation angles,as well as non steering wheel toe in,camber,and axle deviation angles;The positioning parameters are modified by fitting the vehicle body plane normal vector using the total least square method and fitting the vehicle geometric centerline using the rear wheel center coordinates.2.Model construction and global calibration of machine vision systemsAccording to the camera imaging model,establish the conversion relationship between the four coordinate systems.Based on Zhang Zhengyou’s camera calibration algorithm,use Open CV to complete the camera calibration work,and conduct calibration experiments to obtain the internal and external parameters and distortion coefficients of the camera,perform distortion correction,and evaluate the calibration effect by calculating the re projection error value;Using the multi target device calibration method,a non overlapping field of view dual camera model is established,and the conversion matrix between the dual cameras is calculated.The optimal solution of the conversion matrix is solved through nonlinear optimization,and the camera coordinate system is synchronized to the same coordinate system.3.Ellipse edge detection and fittingPreprocess the wheel image to meet the detection requirements,including histogram equalization,Gaussian filtering,and image segmentation.Extract the wheel edge based on polynomial interpolation,and solve the edge subpixel coordinate points;Combine the Opencv ellipse detection function to obtain the minimum circumscribed rectangle parameters of the ellipse,and then obtain the coefficients of the general equation of the ellipse;Using the relationship between the spatial circular posture and the elliptical equation in the image coordinate system,the normal vector of the wheel surface and the coordinates of the wheel center are solved,and a test platform is built to measure the wheel posture parameters to verify the feasibility of the measurement scheme. |
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