Abstract
Visual method including binocular stereo vision method and monocular vision method of the relative position and pose measurement for space target has become relatively mature, and many researchers focus on the method based on three-dimension measurement recently. ICP alignment, which is the key of three-dimension pattern measurement method, has the problem of low efficiency in large data sets. Considering this problem, an improved ICP algorithm is proposed in this paper. The improved ICP algorithm is the combination of the original ICP algorithm and KD-TREE. The experimental comparison between the improved ICP algorithm and the traditional ICP algorithm in efficiency has been given in this paper, which shows that the improved ICP algorithm can get much better performance.
Highlights
The space target has been carried out in orbit demonstration of real experiment currently, a method based on two dimensional vision image technique is one of the most common approach in relative position and pose measurement of space target, such as the closing camera system on ETS-VII [1], the frequency measurement system on ATV et al [2]
The efficiency of the traditional ICP algorithm is the most important problem, so this paper gives an improved ICP algorithm based on KD-TREE to solve this problem, through the effective management of the point sets, the relationship between the discrete points can be established, and the registration efficiency can be improved greatly
It can be concluded that the improved ICP algorithm based on KD-TREE is much better than traditional ICP algorithm
Summary
The space target has been carried out in orbit demonstration of real experiment currently, a method based on two dimensional vision image technique is one of the most common approach in relative position and pose measurement of space target, such as the closing camera system on ETS-VII [1], the frequency measurement system on ATV et al [2]. ICP algorithm is a kind of matching algorithms which is mostly used in three-dimension point cloud registration It is based on the iterative optimization matrix. Each point of the target point set is focused to find the nearest point of the reference point set Using these closest points, the corresponding rotation matrix and translation vector is calculated. Through the calculated rotation matrix and translation vector, a new point set changed by the target point set can be got, the algorithm goes into the iteration. The efficiency of the originally proposed ICP algorithm is not very high, the main problem is to find the corresponding point, it requires each point of the target point set and the reference point set is compared to determine the neatest distance. The efficiency of the algorithm is greatly improved through this method
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