LiDAR relocalization plays a crucial role in many fields, including robotics, autonomous driving, and computer vision. LiDAR-based retrieval from a database typically incurs high computation storage costs and can lead to globally inaccurate pose estimations if the database is too sparse. On the other hand, pose regression methods take images or point clouds as inputs and directly regress global poses in an end-to-end manner. They do not perform database matching and are more computationally efficient than retrieval techniques. We propose HypLiLoc, a new model for LiDAR pose regression. We use two branched backbones to extract 3D features and 2D projection features, respectively. We consider multi-modal feature fusion in both Euclidean and hyperbolic spaces to obtain more effective feature representations. Experimental results indicate that HypLiLoc achieves state-of-the-art performance in both outdoor and indoor datasets. We also conduct extensive ablation studies on the framework design, which demonstrate the effectiveness of multi-modal feature extraction and multi-space embedding. Our code is released at: https://github.com/sijieaaa/HypLiLoc

翻译：激光雷达（LiDAR）的姿态回归在机器人、自动驾驶和计算机视觉等领域中发挥着重要的作用。基于数据库的LiDAR检索通常会导致高计算存储成本，并且如果数据库太稀疏，则可能导致全局精度不高的姿态估计。另一方面，姿态回归方法以图像或点云为输入，并直接进行端到端的全局姿态回归。它们不执行数据库匹配，并且比检索技术更具计算效率。我们提出了HypLiLoc，一种新的激光雷达姿态回归模型。我们使用两个分支主干来提取三维特征和二维投影特征。我们考虑在欧几里得空间和超几何空间中进行多模态特征融合，以获得更有效的特征表示。实验结果表明，HypLiLoc在室外和室内数据集中均实现了最先进的性能。我们还进行了大量实验以证明多模态特征提取和多空间嵌入的有效性。我们的代码已在以下地址发布：https：//github.com/sijieaaa/HypLiLoc