Research on a Data Fusion Technology for Aircraft Skin Seam Features
Computer vision is becoming a common trend in aircraft skin seams feature research,but the simplex computer vision meas-urement method has limitations.To further optimize the measurement of air craft skin seam features.A mixed measurement method is employed.Line laser and FPP are used to measure the seam area of aircraft skin.Data from two measurement methods are fused to cor-rect for gaps and flushes.Firstly,according to different characteristics of line laser and FPP,the distributed two-level fusion algorithm is adopted to eliminate outliers through data alignment.Then,the two kinds of measurement data are fused through adaptive weighted fu-sion.Finally,it is applied to the measurement of skin seams for actual aircraft.A comparison control design with four groups of standard seams and one group of actual aircraft skin seams is utilized.The experimental data suggest that the accuracy of gap and flush is signifi-cantly improved after the measurement results are optimized through distributed two-level data fusion.In comparison with the measure-ment data before optimization,the maximum mean error of flush measurement value is 0.031 9 mm,and the maximum mean error of gap measurement value is 0.034 4 mm,the gap measurement accuracy is improved by nearly 30%,and the flush measurement accuracy is improved by nearly 4%.This fusion algorithm balances the advantages of two structured light vision measurement technologies,reducing overall error of the system,which provides a new idea for efficient and accurate measurement of skin joints.
data fusionadaptive weightingstructured lightaircraft inspectionseam measurement