PROBE: 具有解析平移鲁棒性的概率占用BEV编码用于3D地点识别
PROBE: Probabilistic Occupancy BEV Encoding with Analytical Translation Robustness for 3D Place Recognition
AI总结：
提出无学习的LiDAR地点描述符PROBE，通过极坐标雅可比解析边缘化连续平移，实现距离自适应角度不确定性，在跨传感器泛化中取得高精度。
摘要：
我们提出PROBE（概率占用BEV编码），一种无学习的LiDAR地点识别描述符，将每个BEV单元的占用建模为伯努利随机变量。PROBE不依赖于离散点云扰动，而是通过极坐标雅可比解析边缘化连续笛卡尔平移，在O(R·S)时间内得到距离自适应角度不确定性σ_θ = σ_t / r。主要参数σ_t表示以米为单位的预期平移不确定性，这是一种与传感器无关的物理量，增强了跨传感器泛化能力，同时减少了对每个数据集大量调参的需求。成对相似性结合了伯努利-KL Jaccard与指数不确定性门控以及基于FFT的高度余弦相似性用于旋转对齐。在涵盖四种不同LiDAR类型的四个数据集上评估，PROBE在多会话评估中实现了手工描述符中最高的精度，并且在单会话性能上与手工和监督基线相比具有竞争力。源代码和补充材料可在该https URL获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
