  Point clouds are collected nowadays from a plethora of sensors, some having
higher accuracies and higher costs, some having lower accuracies but also lower
costs. Not only there is a large choice for different sensors, but also these
can be transported by different platforms, which can provide different scan
geometries. In this work we test the extraction of four different keypoint
detectors and three feature descriptors. We benchmark performance in terms of
calculation time and we assess their performance in terms of accuracy in their
ability in coarse automatic co-registration of two clouds that are collected
with different sensors, platforms and scan geometries. One, which we define as
having the higher accuracy, and thus will be used as reference, was surveyed
via a UAV flight with a Riegl MiniVUX-3, the other on a bicycle with a Livox
Horizon over a walking path with un-even ground.The novelty in this work
consists in comparing several strategies for fast alignment of point clouds
from very different surveying geometries, as the drone has a bird's eye view
and the bicycle a ground-based view. An added challenge is related to the lower
cost of the bicycle sensor ensemble that, together with the rough terrain,
reasonably results in lower accuracy of the survey. The main idea is to use
range images to capture a simplified version of the geometry of the surveyed
area and then find the best features to match keypoints. Results show that NARF
features detected more keypoints and resulted in a faster co-registration
procedure in this scenariowhereas the accuracy of the co-registration is
similar to all the combinations of keypoint detectors and features.
