Alternative Approaches for the Registration of Terrestrial Laser Scanners Data using Linear/Planar Features
Static terrestrial laser scanners have been increasingly used in three-dimensional data acquisition since it can rapidly provide accurate measurements with high resolution. Several scans from multiple viewpoints are necessary to achieve complete coverage of the surveyed objects due to occlusion and large object size. Therefore, in order to reconstruct three-dimensional models of the objects, the task of registration is required to transform several individual scans into a common reference frame. This thesis introduces three alternative approaches for the coarse registration of two adjacent scans, namely, feature-based approach, pseudo-conjugate point-based method, and closed-form solution. In the feature-based approach, linear and planar features in the overlapping area of adjacent scans are selected as registration primitives. The pseudo-conjugate point-based method utilizes non-corresponding points along common linear and planar features to estimate transformation parameters. The pseudo-conjugate point-based method is simpler than the feature-based approach since the partial derivatives are easier to compute. In the closed-form solution, a rotation matrix is first estimated by using a unit quaternion, which is a concise description of the rotation. Afterward, the translation parameters are estimated with non-corresponding points along the linear or planar features by using the pseudo-conjugate point-based method. Alternative approaches for fitting a line or plane to data with errors in three-dimensional space are investigated.
Experiments are conducted using simulated and real datasets to verify the effectiveness of the introduced registration procedures and feature fitting approaches. The proposed two approaches of line fitting are tested with simulated datasets. The results suggest that these two approaches can produce identical line parameters and variance-covariance matrix. The three registration approaches are tested with both simulated and real datasets. In the simulated datasets, all three registration approaches produced equivalent transformation parameters using linear or planar features. The comparison between the simulated linear and planar features shows that both features can produce equivalent registration results. In the real datasets, the three registration approaches using the linear or planar features also produced equivalent results. In addition, the results using real data indicates that the registration approaches using planar features produced better results than the approaches using linear features. The experiments show that the pseudo-conjugate point-based approach is easier to implement than the feature-based approach. The pseudo-conjugate point-based method and feature-based approach are nonlinear, so an initial guess of transformation parameters is required in these two approaches. Compared to the nonlinear approaches, the closed-form solution is linear and hence it can achieve the registration of two adjacent scans without the requirement of any initial guess for transformation parameters. Therefore, the pseudo-conjugate point-based method and closed-form solution are the preferred approaches for coarse registration using linear or planar features. In real practice, the planar features would have a better preference when compared to linear features since the linear features are derived indirectly by the intersection of neighboring planar features. To get enough lines with different orientations, planes that are far apart from each other have to be extrapolated to derive lines.