Abstract
The paper presents an iterative matching method to improve a direct geo-referencing approach for stationary terrestrial 3D laser scans by means of the positional and transformational uncertainties of two 3D point clouds. In this approach, a geo-referenced point cloud from a station inherits the following stochastic information: (1) transformational uncertainties from processed GNSS data through the Kalman filter and (2) positional uncertainties through the range and angular uncertainties from the terrestrial laser scanner as well as the incident angle of the laser beam to a surface. This stochastic information from two (pre-) geo-referenced 3D point clouds is implemented within a novel iterative matching algorithm, named Geo-referencing ICP with Helmert 3D transformation , for subsequent post-processing procedures such as segmentation and further calibration.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call