Keypoint based autonomous registration of terrestrial laser point-clouds

Abstract

Registration of terrestrial laser scanning point-clouds involves handling huge datasets, irregular point distribution, multiple views, and relatively low textured surfaces. Common approaches for the registration are based in large on the iterative closest point (ICP) model, which is non-linear and requires good initial values to secure convergence to the actual solution. Computation of the initial approximations to launch the ICP algorithm requires the extraction of distinct features, devising methods for matching them among the datasets, and then computing the transformation parameters. In this paper we present a computational approach for the registration problem. In essence we exploit 3D rigid-body transformation invariant features to reduce significantly the computational load involved in the matching between key features. Generally, with the partial overlap among datasets and among the extracted features the identification of corresponding key features can be viewed as a sub-graph matching problem. This problem is hard to solve, but as the actual matched entities are subjected to a six parameters transformation it becomes manageable. We show that distances, which are invariant to rigid body transformation, can be applied for solving this problem. We then show that by using selected keypoints, the matching process can be optimized. We also show how the information embedded within the range data is utilized to improve the quality of the selected points. Following the presentation of our algorithm, we demonstrate its application on a sequence of scans taken in areas featuring a clutter of objects. Results and the analysis show its efficiency and robustness.