Pairwise point cloud registration is a critical task for many applications, which heavily depends on finding correct correspondences from the two point clouds. However, the low overlap between input point clouds causes the registration to fail easily, leading to mistaken overlapping and mismatched correspondences, especially in scenes where non-overlapping regions contain similar structures. In this paper, we present a unified bird's-eye view (BEV) model for jointly learning of 3D local features and overlap estimation to fulfill pairwise registration and loop closure. Feature description is performed by a sparse UNet-like network based on BEV representation, and 3D keypoints are extracted by a detection head for 2D locations, and a regression head for heights. For overlap detection, a cross-attention module is applied for interacting contextual information of input point clouds, followed by a classification head to estimate the overlapping region. We evaluate our unified model extensively on the KITTI dataset and Apollo-SouthBay dataset. The experiments demonstrate that our method significantly outperforms existing methods on overlap estimation, especially in scenes with small overlaps. It also achieves top registration performance on both datasets in terms of translation and rotation errors.

Environmental fluctuations pose crucial challenges to a localization system in autonomous driving. We present a robust LiDAR localization system that maintains its kinematic estimation in changing urban scenarios by using a dead reckoning solution implemented through a LiDAR inertial odometry. Our localization framework jointly uses information from complementary modalities such as global matching and LiDAR inertial odometry to achieve accurate and smooth localization estimation. To improve the performance of the LiDAR odometry, we incorporate inertial and LiDAR intensity cues into an occupancy grid based LiDAR odometry to enhance frame-to-frame motion and matching estimation. Multi-resolution occupancy grid is implemented yielding a coarse-to-fine approach to balance the odometry's precision and computational requirement. To fuse both the odometry and global matching results, we formulate a MAP estimation problem in a pose graph fusion framework that can be efficiently solved. An effective environmental change detection method is proposed that allows us to know exactly when and what portion of the map requires an update. We comprehensively validate the effectiveness of the proposed approaches using both the Apollo-SouthBay dataset and our internal dataset. The results confirm that our efforts lead to a more robust and accurate localization system, especially in dynamically changing urban scenarios.