  Point cloud registration has been one of the basic steps of point cloud
processing, which has a lot of applications in remote sensing and robotics. In
this report, we summarized the basic workflow of target-less point cloud
registration,namely correspondence determination and transformation estimation.
Then we reviewed three commonly used groups of registration approaches, namely
the feature matching based methods, the iterative closest points algorithm and
the randomly hypothesis and verify based methods. Besides, we analyzed the
advantage and disadvantage of these methods are introduced their common
application scenarios. At last, we discussed the challenges of current point
cloud registration methods and proposed several open questions for the future
development of automatic registration approaches.
