Robust Point Set Registration Based on Semantic Information

Point cloud registration a challenging task in situations with poor initial value and scenarios with limited geometric structure. In these cases, the correct correspondence between two point clouds is unknown and difficult to establish. To cope with this problem, the semantic of partial points is introduced in this paper. Firstly, the semantic information is used to find more reasonable correspondence, i.e. semantic point pairs. Secondly, we formulate a novel objective function to integrate the matching error of semantic point pairs as guidance of registration. Thirdly, a hyperparameter is applied to balance the confidence of semantic point pairs. At last, a novel algorithm under the ICP framework is presented to optimize the rigid transformation iteratively. The evaluation of KITTI data set reveals the robustness and accuracy of our method in the complex scenes mentioned above.