Research On Visual Measurement Technology Of Space Target Pose

The measurement for the position and attitude of the target in space is an important issue in the field of target tracking,space docking of the aircraft and mobile robot positioning.Vision measurement technology has the advantages of low cost,high accuracy,and high degree of automation,which is very suitable for close-range pose measurement.The monocular vision posture measurement system,which has been widely used in various areas,has a simple structure and can perform non-contact measurement on the target,avoiding mechanical damage to the target.In this paper,a monocular vision posture measurement system is designed,a suitable distortion model is determined,the camera is accurately calibrated,and a hybrid particle swarm optimization algorithm is proposed,achieving high precision of pose measurement.First,the camera calibration algorithm and distortion model are studied.Camera calibration is to solve camera parameters when the model is definite.Choosing the appropriate camera model is vital to accurately calibrate the camera.The plane calibration method is applied to initially calibrate the camera.Aimed at the camera used in this paper,the calibration experiments on different distortion models are conducted respectively.According to the experimental results,a reasonable distortion model is selected,realizing accurate calibration of the cameras.Secondly,the pose measurement algorithm is deeply studied,and the advantages and disadvantages of the classic pose measurement algorithm are analyzed and compared,including direct linear transformation,EPnP algorithm,orthogonal iteration algorithm.On this basis,a hybrid particle swarm algorithm is proposed.The algorithm adopts an adaptive inertia weight strategy to balance the global search ability and local search ability of the population.Through cross operation and mutation operation on the particles,the algorithm is not easy to fall into the local optimal.And the efficiency of the algorithm is improved greatly by combining the Gauss Newton method with the improved particle algorithm.Finally,Digital simulation experiments are carried out on a variety of pose measurement algorithms,and the comparison results show that the hybrid particle swarm optimization algorithm has high accuracy and stability.A monocular vision posture measurement system is built in the laboratory.The rotation and movement platforms are used to simulate the change of the pose of the marker.The pose measurement experiment is conducted on the marker to verify the feasibility and stability of the pose measurement system.The experiment results show that the rotation angle error does not exceed 0.06°,and the translation error does not exceed 0.04mm,indicating that the measurement accuracy is relatively high.