Global LiDAR localization is a fundamental task for autonomous navigation systems. Recent methods perform Scene Coordinate Regression (SCR) and achieve superior accuracy over Absolute Pose Regression (APR) solutions by predicting dense 3D world coordinates. However, SCR approaches introduce two major bottlenecks: severe computational inefficiency from processing raw 3D geometries and significant performance degradation under varying sensor viewpoints. To address these limitations, we present MB-Loc, a lightweight and viewpoint-robust SCR framework. Instead of relying on heavy 3D convolutions, we project the input LiDAR scan into a 2.5D Multi-planar Bird's-Eye View (BEV) representation. By slicing the point-cloud along the Z-axis and mapping signed depths into discrete 2D planes, MB-Loc retains essential 3D geometric structures while exploiting the computational tractability of standard 2D CNNs. To handle the inherent sparsity of outdoor LiDAR, we introduce a KL-regularized latent bottleneck that explicitly models spatial uncertainty without injecting stochastic noise. Finally, to ensure rotation robustness, we apply 3D spatial augmentations prior to planar projection, forcing the network to implicitly learn viewpoint-invariant features. We perform extensive experiments on the publicly available NCLT dataset and demonstrate that our proposed method outperforms the current state-of-the-art. Operating at real-time inference speeds, MB-Loc significantly outperforms traditional 3D-SCR architectures in computational efficiency.

翻译：摘要：全局LiDAR定位是自主导航系统的基本任务。现有方法通过场景坐标回归（SCR）预测稠密三维世界坐标，相较于绝对位姿回归（APR）方法取得了更优精度。然而，SCR方法存在两大瓶颈：处理原始三维几何结构导致的严重计算低效性，以及传感器视角变化下的显著性能退化。为解决上述问题，我们提出MB-Loc——一种轻量级且对视角鲁棒的SCR框架。该方法无需依赖繁重的三维卷积，而是将输入LiDAR扫描投影为2.5D多平面鸟瞰视图（BEV）表示。通过沿Z轴切分点云并将符号深度映射至离散二维平面，MB-Loc在保留关键三维几何结构的同时，能够利用标准二维卷积神经网络的计算便捷性。为应对室外LiDAR固有的稀疏性，我们引入KL正则化的潜在瓶颈层，无需注入随机噪声即可显式建模空间不确定性。最终，为保障旋转鲁棒性，我们在平面投影前应用三维空间数据增强，迫使网络隐式学习视角不变特征。在公开NCLT数据集上的大量实验表明，所提方法优于当前最新技术。MB-Loc在实时推理速度下，其计算效率显著超越传统三维SCR架构。