$U^2$ Frame: A Unified and Unsupervised Learning Framework for LiDAR-Based Loop Closing

Loop closing is critically important in Simultaneous Localization and Mapping (SLAM) due to its ability to correct accumulated localization errors. However, existing methods are hindered by the difficulty of acquiring pose labels and the unreliability of ground truth data. In this paper, we propose $U^{2}$ Frame, a unified LiDAR-based loop closing framework that handles both loop closure detection and relative pose estimation without any ground truth training data. Specifically, the natural temporal-spatial correlation in point cloud sequences is first leveraged to supervise the network training, where near scans are treated as positives and vice versa as negatives. A new neural architecture is then constructed to jointly learn highly discriminative local and global features for loop closure detection. Additionally, an effective candidate verification module that exploits high-order geometric information is presented to further filter out false loop closures and estimate precise poses. We extensively evaluate $U^{2}$ Frame on multiple datasets according to two tasks derived from loop closing: loop closure detection and loop pose estimation. Comparative experiments demonstrate that our method outperforms existing state-of-the-art supervised techniques and has a strong generalization ability across unseen scenarios. Our code is released at https://github.com/yxin-zhang/U2Frame.