Research On Image Processing Technology For Bait Flighting Pose Estimation

Bait fighting pose estimation was very important to bait micro-motion analysis,bait optimal design,or even the missile penetration capability.The paper is focusing on the reality demands of measuring the relative pose parameters between the bait and carrier with high-speed cameras,and researching on the image processing technology for bait high-precision pose estimation.The research contents include designing marker on the curved surface of the bait,researching on fast line detection method,and studying on high-precision pose estimation method,which are aim to provide technical support for the engineering implementation of the bait fighting pose estimation system.The paper also studied the pose estimation problem of conventional targets,which makes the results of the paper more widely used.The research results of the paper are as follows:(1)Designed a bait marker to provide feature information and summarized marker designing methods for rotary targets.The optimal dynamic window was determined under the extreme attitude to ensure that the camera can always capture a full window,and sufficient number of features was put in the window to solve the problem of partial occlusion.According to the structure characteristics of the dynamic window,straight lines which are both stable and scale invariant were chosen as marker features and the length of lines were maximized to make it easy to detect.Marker designing methods for rotary targets were summarized based the bait marker designing experiences.Simulation results show the effectiveness of the bait marker.(2)Proposed a candidate points chosen line detection algorithm for real-time feature detection.The proposed algorithm uses selective scanning technique together with non-maximal suppression techniques to significantly reduce the number of edge points needed to perform line growth.The reserved candidate points are discretized and cannot directly grow into a straight line.Due to which,the gradient direction consistency criterion,NFA criterion and path criterion were used to consist of the joint-matching criterion.Using this criterion,the adjacent candidate points are matching to generate line kernels and the line kernels rapidly grow into complete lines.Simulation tests show that the proposed algorithm is of low computation and high line detection rate.(3)Proposed a weighted pose estimation algorithm to obtain bait's high-precision pose parameters.The unrelated points on 2D lines are used to construct the objective function,basing on which the fitting process of 2D lines are avoided.A weight is allocated to each 2D line point based on the distance of the point to its corresponding projecting plane,with which the contribution of the point to the objective function are precise controlled,because of which the line features are used more effectively.At last,the translation is solved linearly by introducing several point correspondences.Experiments show that,the propose method outperforms other methods in term of accuracy and noise robustness.(4)Proposed an accurate and stable pose estimation algorithm by introducing the unchanged information contained in unknowns.To use the line constraints more effectively,equation system is constructed based on the points of 3D lines,as the constraints of these points are stronger than that of line direction vectors.To obtain noise insensitive solution,the equation system is solved in several possible dimensions by introducing the unchanged constraints contained in unknowns,and the pose parameters with the smallest error are chosen as the final result.On the basis of this algorithm,a more accurate pose estimation algorithm that can unit line and point features were proposed.The 3D point and line features are represented by one control point and three control vectors,and the constraint equations constructed basing on these features are combined with a reasonable weight.Then the equations are solved to get the pose parameters by introducing the unchanged constraints contained in unknowns again.Experiments show that,the two algorithms were both more accurate and stable than other non-iterative methods.