Efficient Global Point Cloud Registration by
Matching Rotation Invariant Features Through
Translation Search
Abstract. Three-dimensional rigid point cloud registration has many
applications in computer vision and robotics. Local methods tend to fail,
causing global methods to be needed, when the relative transformation
is large or the overlap ratio is small. Most existing global methods utilize
BnB optimization over the 6D parameter space of SE(3). Such methods
are usually very slow because the time complexity of BnB optimization is
exponential in the dimensionality of the parameter space. In this paper,
we decouple the optimization of translation and rotation, and we propose
a fast BnB algorithm to globally optimize the 3D translation parameter
first. The optimal rotation is then calculated by utilizing the global optimal translation found by the BnB algorithm. The separate optimization
of translation and rotation is realized by using a newly proposed rotation invariant feature. Experiments on challenging data sets demonstrate
that the proposed method outperforms state-of-the-art global methods
in terms of both speed and accuracy