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：激光雷达重定位在机器人、自动驾驶和计算机视觉等多个领域中发挥着关键作用。基于数据库检索的激光雷达方法通常面临较高的计算存储成本，且若数据库过于稀疏，可能导致全局位姿估计不准确。相比之下，位姿回归方法以图像或点云作为输入，通过端到端方式直接回归全局位姿，无需进行数据库匹配，计算效率高于检索技术。我们提出HypLiLoc，一种用于激光雷达位姿回归的新模型。该模型采用双分支主干网络，分别提取三维特征和二维投影特征，并在欧几里得空间与双曲空间中实现多模态特征融合，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上均达到了当前最优性能。我们还对框架设计进行了全面的消融研究，验证了多模态特征提取与多空间嵌入的有效性。我们的代码已开源：https://github.com/sijieaaa/HypLiLoc