{"paper":{"title":"Tackling the Kidnapped Robot Problem via Sparse Feasible Hypothesis Sampling and Reliable Batched Multi-Stage Inference","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"The proposed framework estimates the global pose of a kidnapped robot efficiently and reliably from a single LiDAR scan and an occupancy grid map while the robot remains stationary.","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Deqing Huang, Henry Leung, Kai Shen, Lei Ma, Muhua Zhang, Ying Wu","submitted_at":"2025-11-03T04:30:49Z","abstract_excerpt":"This paper addresses the Kidnapped Robot Problem (KRP), a core localization challenge of relocalizing a robot in a known map without prior pose estimate upon localization loss or at SLAM initialization. For this purpose, a passive 2-D global relocalization framework is proposed. It estimates the global pose efficiently and reliably from a single LiDAR scan and an occupancy grid map while the robot remains stationary, thereby enhancing the long-term autonomy of mobile robots. The proposed framework casts global relocalization as a non-convex problem and solves it via the multi-hypothesis scheme"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"The proposed framework estimates the global pose efficiently and reliably from a single LiDAR scan and an occupancy grid map while the robot remains stationary, thereby enhancing the long-term autonomy of mobile robots.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The framework assumes that the RRT under traversability constraints will generate a set of hypotheses that includes or is close enough to the true pose so that the subsequent multi-stage inference can recover it, as described in the hypothesis generation step using the occupancy grid map.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"A multi-hypothesis global relocalization framework uses RRT under traversability constraints for sparse feasible pose sampling, SMAD for early candidate ordering, and TAM for orientation and final evaluation to solve the kidnapped robot problem efficiently from a single non-panoramic LiDAR scan.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"The proposed framework estimates the global pose of a kidnapped robot efficiently and reliably from a single LiDAR scan and an occupancy grid map while the robot remains stationary.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"5cbdfe802863501796dc5b8b3a179f2e198e1488cad0229d9b9f85d78d72f334"},"source":{"id":"2511.01219","kind":"arxiv","version":7},"verdict":{"id":"045ea46d-145a-4a61-b3fa-ff64a4812f6d","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-18T01:40:21.904623Z","strongest_claim":"The proposed framework estimates the global pose efficiently and reliably from a single LiDAR scan and an occupancy grid map while the robot remains stationary, thereby enhancing the long-term autonomy of mobile robots.","one_line_summary":"A multi-hypothesis global relocalization framework uses RRT under traversability constraints for sparse feasible pose sampling, SMAD for early candidate ordering, and TAM for orientation and final evaluation to solve the kidnapped robot problem efficiently from a single non-panoramic LiDAR scan.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The framework assumes that the RRT under traversability constraints will generate a set of hypotheses that includes or is close enough to the true pose so that the subsequent multi-stage inference can recover it, as described in the hypothesis generation step using the occupancy grid map.","pith_extraction_headline":"The proposed framework estimates the global pose of a kidnapped robot efficiently and reliably from a single LiDAR scan and an occupancy grid map while the robot remains stationary."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2511.01219/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":"f17a70200c83210839da29ce6049d5cfe38cf3df8477e27984b06352bf379b0f"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}