{"paper":{"title":"PROBE: Probabilistic Occupancy BEV Encoding with Analytical Translation Robustness for 3D Place Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"PROBE encodes LiDAR bird's-eye-view grids as Bernoulli occupancy variables and marginalizes continuous translations analytically to achieve translation-robust place recognition without learning.","cross_cats":["cs.CV"],"primary_cat":"cs.RO","authors_text":"Byoungho Lee, Gichul Yoo, Jinseop Lee","submitted_at":"2026-03-06T07:00:26Z","abstract_excerpt":"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 $\\sigma_\\theta = \\sigma_t / r$ in $\\mathcal{O}(R{\\cdot}S)$ time. The primary parameter $\\sigma_t$ represents the expected translational uncertainty in meters, a sensor-independent physical quantity that enhances cross-s"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"PROBE achieves the highest accuracy among handcrafted descriptors in multi-session evaluation and competitive single-session performance relative to both handcrafted and supervised baselines.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That the single parameter sigma_t representing expected translational uncertainty in meters is a sensor-independent physical quantity that enhances cross-sensor generalization while reducing per-dataset tuning.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"PROBE is a learning-free LiDAR place recognition descriptor using probabilistic Bernoulli occupancy in BEV with analytical translation marginalization via polar Jacobian, achieving top handcrafted accuracy on multi-session tasks across four datasets.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"PROBE encodes LiDAR bird's-eye-view grids as Bernoulli occupancy variables and marginalizes continuous translations analytically to achieve translation-robust place recognition without learning.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"083ab5ddca3c04441c00d79b9a3cda80f06446b98a24a9818509e86f4d9f30d2"},"source":{"id":"2603.05965","kind":"arxiv","version":3},"verdict":{"id":"07bd94a3-d161-433d-b3b8-e5921813f94b","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-15T15:45:54.957894Z","strongest_claim":"PROBE achieves the highest accuracy among handcrafted descriptors in multi-session evaluation and competitive single-session performance relative to both handcrafted and supervised baselines.","one_line_summary":"PROBE is a learning-free LiDAR place recognition descriptor using probabilistic Bernoulli occupancy in BEV with analytical translation marginalization via polar Jacobian, achieving top handcrafted accuracy on multi-session tasks across four datasets.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That the single parameter sigma_t representing expected translational uncertainty in meters is a sensor-independent physical quantity that enhances cross-sensor generalization while reducing per-dataset tuning.","pith_extraction_headline":"PROBE encodes LiDAR bird's-eye-view grids as Bernoulli occupancy variables and marginalizes continuous translations analytically to achieve translation-robust place recognition without learning."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2603.05965/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":2,"snapshot_sha256":"a80e6b7242b3d8737a54340676944048fbb979814bbcccefee5406df2ac3a12a"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}