Keypoint-based 4-Points Congruent Sets  Automated marker-less registration of laser scans
We propose a method to automatically register two point clouds acquired with a terrestrial laser scanner without placing any markers in the scene. What makes this task challenging are the strongly varying point densities caused by the line-of-sight measurement principle, and the huge amount of data. The first property leads to low point densities in potential overlap areas with scans taken from different viewpoints while the latter calls for highly efficient methods in terms of runtime and memory requirements. A crucial yet largely unsolved step is the initial coarse alignment of two scans without any simplifying assumptions, that is, point clouds are given in arbitrary local coordinates and no knowledge about their relative orientation is available. Once coarse alignment has been solved, scans can easily be fine-registered with standard methods like least-squares surface or Iterative Closest Point matching. In order to drastically thin out the original point clouds while retaining characteristic features, we resort to extracting 3D keypoints. Such clouds of keypoints, which can be viewed as a sparse but nevertheless discriminative representation of the original scans, are then used as input to a very efficient matching method originally developed in computer graphics, called 4-Points Congruent Sets (4PCS) algorithm. We adapt the 4PCS matching approach to better suit the characteristics of laser scans. The resulting Keypoint-based 4-Points Congruent Sets (K-4PCS) method is extensively evaluated on challenging indoor and outdoor scans. Beyond the evaluation on real terrestrial laser scans, we also perform experiments with simulated indoor scenes, paying particular attention to the sensitivity of the approach with respect to highly symmetric scenes.
terrestrial laser scanning point cloud registration 3d keypoint extraction congruent point sets geometric matching