无需训练的激光雷达定位描述符，通过概率建模提升跨传感器鲁棒性
PROBE: Probabilistic Occupancy BEV Encoding with Analytical Translation Robustness for 3D Place Recognition
- 将每个俯视图单元建模为伯努利随机变量，解析计算平移不确定性
- 在4个数据集上多时段识别准确率领先手工设计描述符，单时段性能媲美监督模型
- 无需调参，物理意义明确的平移不确定参数适用于不同激光雷达设备
我们提出PROBE（PRobabilistic Occupancy BEV Encoding），一种无需训练的激光雷达位置识别描述符，将每个俯视图（BEV）单元的占据状态建模为伯努利随机变量。不同于依赖离散点云扰动的方法，PROBE通过极坐标雅可比矩阵解析地对连续笛卡尔平移进行边缘化，实现时间复杂度为$\mathcal{O}(R{\cdot}S)$的自适应角度不确定性$σ_θ= σ_t / r$。核心参数$σ_t$表示以米为单位的期望平移不确定性，是与传感器无关的物理量，增强了跨传感器泛化能力并减少了针对特定数据集的调参需求。成对相似性融合伯努利-KL Jaccard、指数不确定性门控以及基于FFT的高度余弦相似性以实现旋转对齐。在涵盖四种不同激光雷达类型的四个数据集上评估，PROBE在多时段测试中达到所有手工设计描述符中的最高准确率，并在单时段测试中表现优于或媲美手工及监督基线模型。源代码和补充材料见https://sites.google.com/view/probe-pr。
原文摘要 · Abstract (English)
We present PROBE (PRobabilistic Occupancy BEV Encoding), a learning-free LiDAR place recognition descriptor that models each BEV cell's occupancy as a Bernoulli random variable. Rather than relying on discrete point-cloud perturbations, PROBE analytically marginalizes over continuous Cartesian translations via the polar Jacobian, yielding a distance-adaptive angular uncertainty $σ_θ= σ_t / r$ in $\mathcal{O}(R{\cdot}S)$ time. The primary parameter $σ_t$ represents the expected translational uncertainty in meters, a sensor-independent physical quantity that enhances cross-sensor generalization while reducing the need for extensive per-dataset tuning. Pairwise similarity combines a Bernoulli-KL Jaccard with exponential uncertainty gating and FFT-based height cosine similarity for rotation alignment. Evaluated on four datasets spanning four diverse LiDAR types, PROBE achieves the highest accuracy among handcrafted descriptors in multi-session evaluation and competitive single-session performance relative to both handcrafted and supervised baselines. The source code and supplementary materials are available at https://sites.google.com/view/probe-pr.
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