Accelerating Outlier-Robust Point Cloud Registration by Known Gravity Directions

3D point cloud registration, which seeks the optimal rigid transformation to align two point clouds, is a fundamental task in autonomous systems. However, the 3D correspondences between point clouds are prone to substantial outliers (mismatches), leading to significant decreases in registration accuracy. Existing outlier-robust registration methods commonly have high computational complexity and, hence, are limited in time-sensitive applications. Inertial measurement unit (IMU) sensors are widespread in modern autonomous systems and can offer precise gravity directions. Accordingly, we propose a highly efficient voting-based outlier removal method by leveraging the gravity prior in this paper. This pre-processing step can significantly reduce the candidate correspondence set for subsequent estimation, thus accelerating robust point cloud registration. We then leverage pairwise invariant features to decompose the optimization of rotation and translation. Further, we propose a two-stage consensus maximization solver to optimize the rotation and translation sequentially, leading to deterministic and robust registration. Extensive experiments on both synthetic and real-world datasets indicate that our method effectively boosts registration efficiency while exhibiting comparable robustness to state-of-the-art methods.