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With the objective of improving the registration of LiDAR point cloudsproduced by kinematic scanning systems, we propose a novel trajectoryadjustment procedure that leverages on the automated extraction of selectedreliable 3D point--to--point correspondences between overlapping point cloudsand their joint integration (adjustment) together with all raw inertial andGNSS observations. This is performed in a tightly coupled fashion using aDynamic Network approach that results in an optimally compensated trajectorythrough modeling of errors at the sensor, rather than the trajectory, level.The 3D correspondences are formulated as static conditions within this networkand the registered point cloud is generated with higher accuracy utilizing thecorrected trajectory and possibly other parameters determined within theadjustment. We first describe the method for selecting correspondences and howthey are inserted into the Dynamic Network as new observation models. We thendescribe the experiments conducted to evaluate the performance of the proposedframework in practical airborne laser scanning scenarios with low-cost MEMSinertial sensors. In the conducted experiments, the method proposed toestablish 3D correspondences is effective in determining point--to--pointmatches across a wide range of geometries such as trees, buildings and cars.Our results demonstrate that the method improves the point cloud registrationaccuracy, that is otherwise strongly affected by errors in the determinedplatform attitude or position (in nominal and emulated GNSS outage conditions),and possibly determine unknown boresight angles using only a fraction of thetotal number of 3D correspondences that are established.

LiDAR Point--to--point Correspondences for Rigorous Registration of Kinematic Scanning in Dynamic Networks