RING#：基于位姿估计实现位置识别的全局定位方法结合旋转平移等变Gram学习
RING#: PR-by-PE Global Localization with Roto-translation Equivariant Gram Learning
AI总结：
针对视角或环境变化下传统顺序式全局定位易因位置识别失效导致精度下降的问题，提出基于位姿估计推导位置识别的新范式及端到端BEV空间定位网络RING#，在多数据集多模态下性能优于SOTA。
摘要：
在GPS信号不可靠的自动驾驶与机器人应用场景中，利用相机、激光雷达等机载感知传感器实现全局定位至关重要。现有大多数方法通过顺序执行位置识别（PR）和位姿估计（PE）完成全局定位：部分方法为两个任务分别训练独立模型，另一部分则采用单模型双分支头的结构，结合各自的任务特定损失进行联合训练。然而，定位精度高度依赖位置识别的成功率，在视角或环境外观发生显著变化的场景中，位置识别往往失效，最终导致定位的位姿估计环节失效。为解决这一问题，我们提出一种新范式：基于位姿估计的位置识别（PR-by-PE）定位，直接从位姿估计推导位置识别结果，无需单独执行位置识别步骤。我们提出RING#，这是一个在鸟瞰图（BEV）空间运行的端到端PR-by-PE定位网络，可兼容视觉与激光雷达两种传感器。RING#采用了一种新颖设计，从BEV特征中学习两种等变表示，可实现全局收敛且计算高效的位姿估计。在NCLT和Oxford数据集上开展的综合实验表明，RING#在视觉与激光雷达两种模态下的表现均优于现有最优方法，验证了所提方法的有效性。相关代码将公开发布。
英文摘要：
Global localization using onboard perception sensors, such as cameras and LiDARs, is crucial in autonomous driving and robotics applications when GPS signals are unreliable. Most approaches achieve global localization by sequential place recognition (PR) and pose estimation (PE). Some methods train separate models for each task, while others employ a single model with dual heads, trained jointly with separate task-specific losses. However, the accuracy of localization heavily depends on the success of place recognition, which often fails in scenarios with significant changes in viewpoint or environmental appearance. Consequently, this renders the final pose estimation of localization ineffective. To address this, we introduce a new paradigm, PR-by-PE localization, which bypasses the need for separate place recognition by directly deriving it from pose estimation. We propose RING#, an end-to-end PR-by-PE localization network that operates in the bird's-eye-view (BEV) space, compatible with both vision and LiDAR sensors. RING# incorporates a novel design that learns two equivariant representations from BEV features, enabling globally convergent and computationally efficient pose estimation. Comprehensive experiments on the NCLT and Oxford datasets show that RING# outperforms state-of-the-art methods in both vision and LiDAR modalities, validating the effectiveness of the proposed approach. The code will be publicly released.
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