用连续高斯距离场实现鲁棒6自由度定位，无需惯性传感器。
G-EDF-Loc: 3D Continuous Gaussian Distance Field for Robust Gradient-Based 6DoF Localization
- 基于分块稀疏高斯混合的3D距离场，保证平滑过渡无边界伪影。
- 利用解析梯度实现厘米级定位精度，即使在严重里程计失准时仍稳定。
- 适合无IMU或低质量传感器的实时定位场景，如无人车、机器人巡检。
本文提出一种基于直接、基于CPU的点云到地图配准流程的鲁棒6自由度定位框架。系统采用G-EDF这一新型连续且内存高效的3D距离场表示方法，通过自适应空间划分的分块稀疏高斯混合模型建模欧氏距离场（EDF），确保块间$C^1$连续性，有效抑制边界伪影。借助该连续地图的解析梯度，保持Eikonal一致性，实现高保真空间重建与实时定位。在大规模数据集上的实验表明，G-EDF-Loc在性能上可媲美当前最优方法，在里程计严重退化或完全缺乏惯性测量单元（IMU）先验的情况下仍表现出卓越鲁棒性。
原文摘要 · Abstract (English)
This paper presents a robust 6-DoF localization framework based on a direct, CPU-based scan-to-map registration pipeline. The system leverages G-EDF, a novel continuous and memory-efficient 3D distance field representation. The approach models the Euclidean Distance Field (EDF) using a Block-Sparse Gaussian Mixture Model with adaptive spatial partitioning, ensuring $C^1$ continuity across block transitions and mitigating boundary artifacts. By leveraging the analytical gradients of this continuous map, which maintain Eikonal consistency, the proposed method achieves high-fidelity spatial reconstruction and real-time localization. Experimental results on large-scale datasets demonstrate that G-EDF-Loc performs competitively against state-of-the-art methods, exhibiting exceptional resilience even under severe odometry degradation or in the complete absence of IMU priors.
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