{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:XDDMXAVAXWYQLBJ5GJFMHITXRD","short_pith_number":"pith:XDDMXAVA","schema_version":"1.0","canonical_sha256":"b8c6cb82a0bdb105853d324ac3a27788d23c22b58143952083ca1c210dc075e3","source":{"kind":"arxiv","id":"2509.08197","version":1},"attestation_state":"computed","paper":{"title":"Online Dynamic SLAM with Incremental Smoothing and Mapping","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Jesse Morris, Viorela Ila, Yiduo Wang","submitted_at":"2025-09-10T00:03:37Z","abstract_excerpt":"Dynamic SLAM methods jointly estimate for the static and dynamic scene components, however existing approaches, while accurate, are computationally expensive and unsuitable for online applications. In this work, we present the first application of incremental optimisation techniques to Dynamic SLAM. We introduce a novel factor-graph formulation and system architecture designed to take advantage of existing incremental optimisation methods and support online estimation. On multiple datasets, we demonstrate that our method achieves equal to or better than state-of-the-art in camera pose and obje"},"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":"2509.08197","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2025-09-10T00:03:37Z","cross_cats_sorted":[],"title_canon_sha256":"551abcd00dd10042b9f57ea71c9181a9a7c51b0d08ed467db171fda7a2f11339","abstract_canon_sha256":"01a2ce7473221cafbae5fe2f615274bcdef19954adf5564d2f6696b3aec0d28f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:08:24.766141Z","signature_b64":"hgyAQlLE2IzoCa1/MMroQbyDNH/oRCH0wRnWM93fn7E6LoeJ6LjiD8aoaQIdhOBHnaKQyjyOhU/xLKssUSG0DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b8c6cb82a0bdb105853d324ac3a27788d23c22b58143952083ca1c210dc075e3","last_reissued_at":"2026-07-05T12:08:24.765578Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:08:24.765578Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Online Dynamic SLAM with Incremental Smoothing and Mapping","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Jesse Morris, Viorela Ila, Yiduo Wang","submitted_at":"2025-09-10T00:03:37Z","abstract_excerpt":"Dynamic SLAM methods jointly estimate for the static and dynamic scene components, however existing approaches, while accurate, are computationally expensive and unsuitable for online applications. In this work, we present the first application of incremental optimisation techniques to Dynamic SLAM. We introduce a novel factor-graph formulation and system architecture designed to take advantage of existing incremental optimisation methods and support online estimation. On multiple datasets, we demonstrate that our method achieves equal to or better than state-of-the-art in camera pose and obje"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.08197","kind":"arxiv","version":1},"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/2509.08197/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":"2509.08197","created_at":"2026-07-05T12:08:24.765640+00:00"},{"alias_kind":"arxiv_version","alias_value":"2509.08197v1","created_at":"2026-07-05T12:08:24.765640+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.08197","created_at":"2026-07-05T12:08:24.765640+00:00"},{"alias_kind":"pith_short_12","alias_value":"XDDMXAVAXWYQ","created_at":"2026-07-05T12:08:24.765640+00:00"},{"alias_kind":"pith_short_16","alias_value":"XDDMXAVAXWYQLBJ5","created_at":"2026-07-05T12:08:24.765640+00:00"},{"alias_kind":"pith_short_8","alias_value":"XDDMXAVA","created_at":"2026-07-05T12:08:24.765640+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.12897","citing_title":"DynoJEPP: Joint Estimation, Prediction and Planning in Dynamic Environments","ref_index":25,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/XDDMXAVAXWYQLBJ5GJFMHITXRD","json":"https://pith.science/pith/XDDMXAVAXWYQLBJ5GJFMHITXRD.json","graph_json":"https://pith.science/api/pith-number/XDDMXAVAXWYQLBJ5GJFMHITXRD/graph.json","events_json":"https://pith.science/api/pith-number/XDDMXAVAXWYQLBJ5GJFMHITXRD/events.json","paper":"https://pith.science/paper/XDDMXAVA"},"agent_actions":{"view_html":"https://pith.science/pith/XDDMXAVAXWYQLBJ5GJFMHITXRD","download_json":"https://pith.science/pith/XDDMXAVAXWYQLBJ5GJFMHITXRD.json","view_paper":"https://pith.science/paper/XDDMXAVA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2509.08197&json=true","fetch_graph":"https://pith.science/api/pith-number/XDDMXAVAXWYQLBJ5GJFMHITXRD/graph.json","fetch_events":"https://pith.science/api/pith-number/XDDMXAVAXWYQLBJ5GJFMHITXRD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XDDMXAVAXWYQLBJ5GJFMHITXRD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XDDMXAVAXWYQLBJ5GJFMHITXRD/action/storage_attestation","attest_author":"https://pith.science/pith/XDDMXAVAXWYQLBJ5GJFMHITXRD/action/author_attestation","sign_citation":"https://pith.science/pith/XDDMXAVAXWYQLBJ5GJFMHITXRD/action/citation_signature","submit_replication":"https://pith.science/pith/XDDMXAVAXWYQLBJ5GJFMHITXRD/action/replication_record"}},"created_at":"2026-07-05T12:08:24.765640+00:00","updated_at":"2026-07-05T12:08:24.765640+00:00"}