{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:35XTY6N2ZFQ4ZO6R6DKXV5CUSN","short_pith_number":"pith:35XTY6N2","schema_version":"1.0","canonical_sha256":"df6f3c79bac961ccbbd1f0d57af4549376243cdc960d86cc0ee5afdc4d60329d","source":{"kind":"arxiv","id":"2509.22910","version":2},"attestation_state":"computed","paper":{"title":"Good Weights: Proactive, Adaptive Dead Reckoning Fusion for Continuous and Robust Visual SLAM","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Jing-Chen Peng, Patricio A. Vela, Yanwei Du","submitted_at":"2025-09-26T20:38:37Z","abstract_excerpt":"Given that Visual SLAM relies on appearance cues for localization and scene understanding, texture-less or visually degraded environments (e.g., plain walls or low lighting) lead to poor pose estimation and track loss. However, robots are typically equipped with sensors that provide some form of dead reckoning odometry with reasonable short-time performance but unreliable long-time performance. The Good Weights (GW) algorithm described here provides a framework to adaptively integrate dead reckoning (DR) with passive visual SLAM for continuous and accurate frame-level pose estimation. Importan"},"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":"2509.22910","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2025-09-26T20:38:37Z","cross_cats_sorted":[],"title_canon_sha256":"733de4c34b3b14bbe07dd9c4bc29327d8289b6379da91f075b3052e567565c7d","abstract_canon_sha256":"eebcd2242dd8a7c78592a1c2341d0f3268d48a7d48ba164af3c4b37167bf7c4c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-08-04T01:57:55.013102Z","signature_b64":"XUoQvu6ma/OdaF6gsJQYNkYv/8PSUSF/UMZCjZaWBTChOi5nRqBd4xbXOIy952HTfIP/vpTLPxGFBjRAhW8wDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"df6f3c79bac961ccbbd1f0d57af4549376243cdc960d86cc0ee5afdc4d60329d","last_reissued_at":"2026-08-04T01:57:55.011540Z","signature_status":"signed_v1","first_computed_at":"2026-08-04T01:57:55.011540Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Good Weights: Proactive, Adaptive Dead Reckoning Fusion for Continuous and Robust Visual SLAM","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Jing-Chen Peng, Patricio A. Vela, Yanwei Du","submitted_at":"2025-09-26T20:38:37Z","abstract_excerpt":"Given that Visual SLAM relies on appearance cues for localization and scene understanding, texture-less or visually degraded environments (e.g., plain walls or low lighting) lead to poor pose estimation and track loss. However, robots are typically equipped with sensors that provide some form of dead reckoning odometry with reasonable short-time performance but unreliable long-time performance. The Good Weights (GW) algorithm described here provides a framework to adaptively integrate dead reckoning (DR) with passive visual SLAM for continuous and accurate frame-level pose estimation. Importan"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.22910","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/2509.22910/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":"2509.22910","created_at":"2026-08-04T01:57:55.012858+00:00"},{"alias_kind":"arxiv_version","alias_value":"2509.22910v2","created_at":"2026-08-04T01:57:55.012858+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.22910","created_at":"2026-08-04T01:57:55.012858+00:00"},{"alias_kind":"pith_short_12","alias_value":"35XTY6N2ZFQ4","created_at":"2026-08-04T01:57:55.012858+00:00"},{"alias_kind":"pith_short_16","alias_value":"35XTY6N2ZFQ4ZO6R","created_at":"2026-08-04T01:57:55.012858+00:00"},{"alias_kind":"pith_short_8","alias_value":"35XTY6N2","created_at":"2026-08-04T01:57:55.012858+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/35XTY6N2ZFQ4ZO6R6DKXV5CUSN","json":"https://pith.science/pith/35XTY6N2ZFQ4ZO6R6DKXV5CUSN.json","graph_json":"https://pith.science/api/pith-number/35XTY6N2ZFQ4ZO6R6DKXV5CUSN/graph.json","events_json":"https://pith.science/api/pith-number/35XTY6N2ZFQ4ZO6R6DKXV5CUSN/events.json","paper":"https://pith.science/paper/35XTY6N2"},"agent_actions":{"view_html":"https://pith.science/pith/35XTY6N2ZFQ4ZO6R6DKXV5CUSN","download_json":"https://pith.science/pith/35XTY6N2ZFQ4ZO6R6DKXV5CUSN.json","view_paper":"https://pith.science/paper/35XTY6N2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2509.22910&json=true","fetch_graph":"https://pith.science/api/pith-number/35XTY6N2ZFQ4ZO6R6DKXV5CUSN/graph.json","fetch_events":"https://pith.science/api/pith-number/35XTY6N2ZFQ4ZO6R6DKXV5CUSN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/35XTY6N2ZFQ4ZO6R6DKXV5CUSN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/35XTY6N2ZFQ4ZO6R6DKXV5CUSN/action/storage_attestation","attest_author":"https://pith.science/pith/35XTY6N2ZFQ4ZO6R6DKXV5CUSN/action/author_attestation","sign_citation":"https://pith.science/pith/35XTY6N2ZFQ4ZO6R6DKXV5CUSN/action/citation_signature","submit_replication":"https://pith.science/pith/35XTY6N2ZFQ4ZO6R6DKXV5CUSN/action/replication_record"}},"created_at":"2026-08-04T01:57:55.012858+00:00","updated_at":"2026-08-04T01:57:55.012858+00:00"}