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：LiDAR重定位在机器人、自动驾驶和计算机视觉等多个领域扮演着关键角色。基于数据库检索的LiDAR方法通常计算与存储成本较高，且若数据库过于稀疏，可能导致全局位姿估计不准确。而位姿回归方法以图像或点云作为输入，能够以端到端方式直接回归全局位姿。这类方法无需进行数据库匹配，且相较于检索技术计算效率更高。本文提出HypLiLoc——一种用于LiDAR位姿回归的新模型。我们采用双分支骨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考虑多模态特征融合，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最先进性能。我们还对框架设计进行了广泛的消融研究，验证了多模态特征提取与多空间嵌入的有效性。代码开源地址：https://github.com/sijieaaa/HypLiLoc