Drift Reduction in Terrestrial Laser Scanning via Linear Dual Quaternion Interpolation

Terrestrial laser scanning (TLS) enables the rapid acquisition of three‐dimensional data in the form of 3D point clouds. However, it presents significant challenges, such as lengthy registration times between point cloud pairs and considerable trajectory drift due to widely spaced stations, which leads to inconsistent 3D reconstruction along the TLS path. This research proposes a pipeline that adapts the fast global registration (FGR) algorithm to efficiently handle TLS‐generated point clouds. The approach includes fine‐tuning FGR parameters and additional preprocessing steps, specifically normal orientation and keypoint extraction. The second contribution introduces a global refinement model (GRM) based on linear interpolation of dual quaternions. This closed‐form solution simultaneously refines rotations and translations in a closed circuit without iterative computations or matrix decomposition. Experimental evaluations on four TLS datasets indicate that the proposed pairwise registration with FGR achieves a 90% success rate across 86 point‐cloud pairs from multiple environments. Moreover, our drift‐correction model reduces closure errors by up to 41% in the dataset circuits, improving pose accuracy in closed trajectories with theoretical advantages that translate into efficient and fast implementation.