{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:PAN62DW3AGCYACZIZMKJ2U2HYD","short_pith_number":"pith:PAN62DW3","schema_version":"1.0","canonical_sha256":"781bed0edb0185800b28cb149d5347c0d9f0ff24a2c07873974d863d3ce39346","source":{"kind":"arxiv","id":"2408.01569","version":2},"attestation_state":"computed","paper":{"title":"TURTLMap: Real-time Localization and Dense Mapping of Low-texture Underwater Environments with a Low-cost Unmanned Underwater Vehicle","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Advaith Venkatramanan Sethuraman, Jingyu Song, Katherine A. Skinner, Onur Bagoren, Razan Andigani","submitted_at":"2024-08-02T20:47:36Z","abstract_excerpt":"Significant work has been done on advancing localization and mapping in underwater environments. Still, state-of-the-art methods are challenged by low-texture environments, which is common for underwater settings. This makes it difficult to use existing methods in diverse, real-world scenes. In this paper, we present TURTLMap, a novel solution that focuses on textureless underwater environments through a real-time localization and mapping method. We show that this method is low-cost, and capable of tracking the robot accurately, while constructing a dense map of a low-textured environment in r"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2408.01569","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2024-08-02T20:47:36Z","cross_cats_sorted":[],"title_canon_sha256":"9d32703a92d7e79bf46bf87932e6f0f2aee1d1a614266b6de4ecc3674fa39bc0","abstract_canon_sha256":"976190bcc288666948e1fff0340ae8cc3b6cbaf2e95509651391a479ad6fe582"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:17:46.979963Z","signature_b64":"w5v7/AwEVxNqktj4Zmu/DH2FSuRXNQeOp27XhSyvnPqpnUf22UxImxQRM7Fz4KIscBOdgO1rcaJcw13wPQpeDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"781bed0edb0185800b28cb149d5347c0d9f0ff24a2c07873974d863d3ce39346","last_reissued_at":"2026-07-05T09:17:46.979465Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:17:46.979465Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"TURTLMap: Real-time Localization and Dense Mapping of Low-texture Underwater Environments with a Low-cost Unmanned Underwater Vehicle","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Advaith Venkatramanan Sethuraman, Jingyu Song, Katherine A. Skinner, Onur Bagoren, Razan Andigani","submitted_at":"2024-08-02T20:47:36Z","abstract_excerpt":"Significant work has been done on advancing localization and mapping in underwater environments. Still, state-of-the-art methods are challenged by low-texture environments, which is common for underwater settings. This makes it difficult to use existing methods in diverse, real-world scenes. In this paper, we present TURTLMap, a novel solution that focuses on textureless underwater environments through a real-time localization and mapping method. We show that this method is low-cost, and capable of tracking the robot accurately, while constructing a dense map of a low-textured environment in r"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.01569","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2408.01569/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":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2408.01569","created_at":"2026-07-05T09:17:46.979525+00:00"},{"alias_kind":"arxiv_version","alias_value":"2408.01569v2","created_at":"2026-07-05T09:17:46.979525+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.01569","created_at":"2026-07-05T09:17:46.979525+00:00"},{"alias_kind":"pith_short_12","alias_value":"PAN62DW3AGCY","created_at":"2026-07-05T09:17:46.979525+00:00"},{"alias_kind":"pith_short_16","alias_value":"PAN62DW3AGCYACZI","created_at":"2026-07-05T09:17:46.979525+00:00"},{"alias_kind":"pith_short_8","alias_value":"PAN62DW3","created_at":"2026-07-05T09:17:46.979525+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2502.09824","citing_title":"PUGS: Perceptual Uncertainty for Grasp Selection in Underwater Environments","ref_index":9,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/PAN62DW3AGCYACZIZMKJ2U2HYD","json":"https://pith.science/pith/PAN62DW3AGCYACZIZMKJ2U2HYD.json","graph_json":"https://pith.science/api/pith-number/PAN62DW3AGCYACZIZMKJ2U2HYD/graph.json","events_json":"https://pith.science/api/pith-number/PAN62DW3AGCYACZIZMKJ2U2HYD/events.json","paper":"https://pith.science/paper/PAN62DW3"},"agent_actions":{"view_html":"https://pith.science/pith/PAN62DW3AGCYACZIZMKJ2U2HYD","download_json":"https://pith.science/pith/PAN62DW3AGCYACZIZMKJ2U2HYD.json","view_paper":"https://pith.science/paper/PAN62DW3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2408.01569&json=true","fetch_graph":"https://pith.science/api/pith-number/PAN62DW3AGCYACZIZMKJ2U2HYD/graph.json","fetch_events":"https://pith.science/api/pith-number/PAN62DW3AGCYACZIZMKJ2U2HYD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PAN62DW3AGCYACZIZMKJ2U2HYD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PAN62DW3AGCYACZIZMKJ2U2HYD/action/storage_attestation","attest_author":"https://pith.science/pith/PAN62DW3AGCYACZIZMKJ2U2HYD/action/author_attestation","sign_citation":"https://pith.science/pith/PAN62DW3AGCYACZIZMKJ2U2HYD/action/citation_signature","submit_replication":"https://pith.science/pith/PAN62DW3AGCYACZIZMKJ2U2HYD/action/replication_record"}},"created_at":"2026-07-05T09:17:46.979525+00:00","updated_at":"2026-07-05T09:17:46.979525+00:00"}