Multi-resolution ICP for the efficient registration of point clouds based on octrees

In this paper we propose a multiresolution scheme based on hierarchical octrees for the registration of point clouds acquired by lidar scanners. The point density of these point clouds is generally sparse and inhomogeneous, a property that can yield a risk for correct alignment. Experiments demonstrate that our multiresolution technique is a lot faster than the traditional iterative closest point (ICP) algorithm while it is more robust, e.g. in case of abrupt movements of the sensor. We can report a speed-up factor of more than 30, without jeopardizing the level of accuracy. In scenarios for which the level of detail is less critical, e.g. in case of navigation for autonomous robots, we can even achieve a larger speed-up by trading speed for quality.