  Registration is a fundamental but critical task in point cloud processing,
which usually depends on finding element correspondence from two point clouds.
However, the finding of reliable correspondence relies on establishing a robust
and discriminative description of elements and the correct matching of
corresponding elements. In this letter, we develop a coarse-to-fine
registration strategy, which utilizes rotation-invariant features and a new
weighted graph matching method for iteratively finding correspondence. In the
graph matching method, the similarity of nodes and edges in Euclidean and
feature space are formulated to construct the optimization function. The
proposed strategy is evaluated using two benchmark datasets and compared with
several state-of-the-art methods. Regarding the experimental results, our
proposed method can achieve a fine registration with rotation errors of less
than 0.2 degrees and translation errors of less than 0.1m.
