Researches On3D Reconstruction Technologies For Non-cooperative Space Targets Using Image Sequences

Three-dimensional (3D) model acquisition of non-cooperative space target is oneof the key technologies for on-orbit servicing, and has broad application prospects in thespace. Under the background that flying around or along with the non-cooperative spacetarget, this dissertation reseaches on3D structure reconstruction technologies fromimage sequences of non-cooperative space target. Three key technologies for3Dreconstruction are mainly investigated, including on-line camera calibration on spaceplatform, high-speed image matching with multiple transformations between images, aswell as fast, accurate and robust fundamental matrix estimation.To extract the geometric constraint information automatically, edge detectionalgorithm is studied, and a morphological edge detection algorithm based onmulti-strucure elements compound filter is presented. The proposed algorithm performsbetter in edge detection, noise immunity and speed. In order to lower the sceneconstraint requirements and extend the application scope, a camera calibration methodbased on parallelogram similarity invariants is presented. The first two terms radialdistortion parameters are also estimated according to the fact that straight lines in thescence are projected as straight lines in the image. The proposed algorithm not onlyreduces the restriction of sence constraint effectively, but also is simple and flexible.With the hardware computing resources limited, to match those images withmultiple transformations fastly, a hardware-accelerated method for Harris cornerdetector is developed firstly, and it is applied to the Gaussian pyramid images to achievehardware acceleration for multi-scale Harris corner detector. The method uses pipelinearchitecture with internal parallel operation to improve the real-time performance andreduce the hardware resources consumption. Secondly, a local feature descriptionalgorithm based on signed contrast context histogram (SCCH) is proposed, which issimple, distinctive and robust. Finally, the above two algorithm are integrated to matchimages fast under multiple transformations.To estimate the fundamental matrix rapidly, robustly and accurately, a fundamentalmatrix estimation method based on sequential similarity detection algorithm (SSDA) ispresented. SSDA is introduced into the maximum a posteriori sample consensus(MAPSAC) to increase the speed. The initial inliers obtained by improved MAPSACare optimized with M-estimator. Those inliers with larger residual errors are removed toenhance the precision and improve the robustness of the algorithm.A3D reconstruction system is designed based on the results of this study, whichlays a foundation for establishing actual3D reconstruction system for non-cooperativespace target using image sequences. The experimental results show that theexperimental system is effective and feasible.