Automatic registration of 3-D point clouds from UAS and airborne LiDAR platforms

An approach to automatically coregister 3-D point cloud surfaces from unmanned aerial systems (UASs) and light detection and ranging (LiDAR) systems is presented. A 3-D point cloud coregistration method is proposed to automatically compute all transformation parameters without the need for initial, approximate values. The approach uses a pair of point cloud height map images for automated feature point correspondence. Initially, keypoints are extracted on the height map images, and then a log-polar descriptor is used as an attribute for matching the keypoints via a Euclidean distance similarity measure. Our study area is the Peace-Athabasca Delta (PAD) situated in northeastern Alberta, Canada. The PAD is a world heritage site, therefore regular monitoring of this wetland is important. Our method automatically coregisters UAS point clouds with airborne LiDAR data collected over the PAD. Together with UAS data acquisition, our approach can potentially be used in the future to facilitate automated coregistration of heterogeneous data throughout the PAD region. Reported transformation parameter accuracies are: a scale error of 0.02, an average rotation error of 0.123 degrees, and an average translation error of 0.237 m.