PROBE: Probabilistic Occupancy BEV Encoding with Analytical Translation Robustness for 3D Place Recognition

• URL: http://arxiv.org/abs/2603.05965v1
• Date: Fri, 06 Mar 2026 07:00:26 GMT
• Title: PROBE: Probabilistic Occupancy BEV Encoding with Analytical Translation Robustness for 3D Place Recognition
• Abstract summary: PROBE is a learning-free LiDAR place recognition system.<n>It analytically marginalizes over continuous Cartesian Jacobian perturbations.<n>It achieves the highest accuracy among handcrafted descriptors.
• Score: 6.460745030735197
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 $\mathcal{O}(R \times S)$ time. The primary parameter $σ_t$ represents the expected translational uncertainty in meters, a sensor-independent physical quantity allowing cross-sensor generalization without 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 against 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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