A framework for registration of multiple point clouds derived from a static terrestrial laser scanner system

The registration problem has been one of the most popular research topics in the geodesic applications. However, what makes the registration problem challenging are partially overlapping surfaces and when no information from the global positioning system (GPS) and inertial navigation system (INS) is given. In this paper, we propose a framework for registration of multiple 3D point clouds derived from a terrestrial laser scanning (TLS) system, which uses a closed-form coarse-to-fine scheme and globally eliminate the residual errors. The main contribution of this work lies in the closed-form multi-feature corresponding model for fine orientation task. Since the multi-feature with geometric constraint is used, a point cloud registration is more likely to be achieved. Due to the coarse orientation task corresponding point-to-plane and line features are non-iteratively obtained in this step. Consequently, a reliable alignment for the global registration solution can be achieved. The advantages of this framework are demonstrated using a challenging real dataset. Additionally, the approach is tested and evaluated against another state-of-the-art algorithm. The results of the experiments indicate that a sensor pose better than 10 mm can be achieved by using this approach.