Abstract: In order to realize the on orbit service and maintenance of the space attitude tumbling spacecraft and the removal of space debris,it is necessary to perform accurate relative pose measurement.Aiming at the problems,a relative pose measurement method based on monocular vision and Kalman filter was proposed.By investigating the feature point matching algorithm,the feature point extraction methods based on the scale invariant feature transform (SIFT) algorithm and the speeded up robust feature (SURF) algorithm with scale invariance and rotation invariance were used.And these two algorithms were further compared to get the working conditions of each other.Through the study of Kalman filter algorithm,the camera bias matrix was introduced,the Kalman filter was designed,the range ambiguity problem of the monocular camera was solved,and the relative pose,main inertia ratio and feature point position information of non-cooperative targets were estimated.According to the simulation,the attitude angle estimation error is less than 0.3° after stabilization,and the relative position estimation error is less than 0.5m.Compared with the true values,the errors are less than 1.67%.The main inertia ratio estimation error is less than 0.01,and the feature point position error is less than 0.005m after stabilization.After introducing the camera bias condition,all the filtering state variables converged,and an estimation with sufficient accuracy was obtained.The problem of the lack of depth information of the monocular camera has been successfully solved.

Key words: monocular vision, non-cooperative goal, SIFT, SURF, the Kalman filter, relative pose estimation