Point Clouds Registration by Using Depth Images

In this paper, we propose an efficient way to produce an initial transposed matrix for two point clouds, which can effectively avoid the drawback of local optimism caused by using standard Iterative Closest Points(ICP)[1] algorithm when matching two point clouds. In our approach, the correspondences used to calculate the transposed matrix are confirmed before the point cloud forms. We use the depth images which have been carefully target-segmented to find the boundaries of the shapes that reflect different views of the same target object. Then each contour is affected by curvature scale space(CSS)[2] method to find a sequence of characteristic points. After that, our method is applied on these characteristic points to find the most matching pairs of points. Finally, we convert the matched characteristic points to 3D points, and the correspondences are there being confirmed. We can use them to compute an initial transposed matrix to tell the computer which part of the first point cloud should be matched to the second. In this way, we put the two point clouds in a correct initial location, so that the local optimism of ICP and its variations can be excluded.