  In this paper, we introduce a new outlier removal method that fully leverages
geometric and semantic information, to achieve robust registration. Current
semantic-based registration methods only use semantics for point-to-point or
instance semantic correspondence generation, which has two problems. First,
these methods are highly dependent on the correctness of semantics. They
perform poorly in scenarios with incorrect semantics and sparse semantics.
Second, the use of semantics is limited only to the correspondence generation,
resulting in bad performance in the weak geometry scene. To solve these
problems, on the one hand, we propose secondary ground segmentation and loose
semantic consistency based on regional voting. It improves the robustness to
semantic correctness by reducing the dependence on single-point semantics. On
the other hand, we propose semantic-geometric consistency for outlier removal,
which makes full use of semantic information and significantly improves the
quality of correspondences. In addition, a two-stage hypothesis verification is
proposed, which solves the problem of incorrect transformation selection in the
weak geometry scene. In the outdoor dataset, our method demonstrates superior
performance, boosting a 22.5 percentage points improvement in registration
recall and achieving better robustness under various conditions. Our code is
available.
