{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2012:HFYCOBQQG3PWJ6V5YMX2L5GR3I","short_pith_number":"pith:HFYCOBQQ","schema_version":"1.0","canonical_sha256":"397027061036df64fabdc32fa5f4d1da019e115f85770600cffeda852e2dd9d9","source":{"kind":"arxiv","id":"1207.2491","version":1},"attestation_state":"computed","paper":{"title":"A Spectral Learning Approach to Range-Only SLAM","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.RO","stat.ML"],"primary_cat":"cs.LG","authors_text":"Byron Boots, Geoffrey J. Gordon","submitted_at":"2012-07-10T21:19:33Z","abstract_excerpt":"We present a novel spectral learning algorithm for simultaneous localization and mapping (SLAM) from range data with known correspondences. This algorithm is an instance of a general spectral system identification framework, from which it inherits several desirable properties, including statistical consistency and no local optima. Compared with popular batch optimization or multiple-hypothesis tracking (MHT) methods for range-only SLAM, our spectral approach offers guaranteed low computational requirements and good tracking performance. Compared with popular extended Kalman filter (EKF) or ext"},"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":"1207.2491","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2012-07-10T21:19:33Z","cross_cats_sorted":["cs.RO","stat.ML"],"title_canon_sha256":"d87d9e9388c49711b1eab4ae78cde72d61e02e03245d586763d82f2dbd8259db","abstract_canon_sha256":"32de460f4fd12d0957f77eeec9481cb19675cd0753d80e0a4d8b5440fbd64932"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:51:15.304771Z","signature_b64":"y2Aoh5lqh5u6AOfGqCCud0cpeBJTiof2vchYwZXa4TFCArs/lSDnHu6sJCbHsQFVPqg+U4uw2LqPJqm69WEKDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"397027061036df64fabdc32fa5f4d1da019e115f85770600cffeda852e2dd9d9","last_reissued_at":"2026-05-18T03:51:15.304183Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:51:15.304183Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Spectral Learning Approach to Range-Only SLAM","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.RO","stat.ML"],"primary_cat":"cs.LG","authors_text":"Byron Boots, Geoffrey J. Gordon","submitted_at":"2012-07-10T21:19:33Z","abstract_excerpt":"We present a novel spectral learning algorithm for simultaneous localization and mapping (SLAM) from range data with known correspondences. This algorithm is an instance of a general spectral system identification framework, from which it inherits several desirable properties, including statistical consistency and no local optima. Compared with popular batch optimization or multiple-hypothesis tracking (MHT) methods for range-only SLAM, our spectral approach offers guaranteed low computational requirements and good tracking performance. Compared with popular extended Kalman filter (EKF) or ext"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1207.2491","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":""},"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":"1207.2491","created_at":"2026-05-18T03:51:15.304287+00:00"},{"alias_kind":"arxiv_version","alias_value":"1207.2491v1","created_at":"2026-05-18T03:51:15.304287+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1207.2491","created_at":"2026-05-18T03:51:15.304287+00:00"},{"alias_kind":"pith_short_12","alias_value":"HFYCOBQQG3PW","created_at":"2026-05-18T12:27:09.501522+00:00"},{"alias_kind":"pith_short_16","alias_value":"HFYCOBQQG3PWJ6V5","created_at":"2026-05-18T12:27:09.501522+00:00"},{"alias_kind":"pith_short_8","alias_value":"HFYCOBQQ","created_at":"2026-05-18T12:27:09.501522+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/HFYCOBQQG3PWJ6V5YMX2L5GR3I","json":"https://pith.science/pith/HFYCOBQQG3PWJ6V5YMX2L5GR3I.json","graph_json":"https://pith.science/api/pith-number/HFYCOBQQG3PWJ6V5YMX2L5GR3I/graph.json","events_json":"https://pith.science/api/pith-number/HFYCOBQQG3PWJ6V5YMX2L5GR3I/events.json","paper":"https://pith.science/paper/HFYCOBQQ"},"agent_actions":{"view_html":"https://pith.science/pith/HFYCOBQQG3PWJ6V5YMX2L5GR3I","download_json":"https://pith.science/pith/HFYCOBQQG3PWJ6V5YMX2L5GR3I.json","view_paper":"https://pith.science/paper/HFYCOBQQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1207.2491&json=true","fetch_graph":"https://pith.science/api/pith-number/HFYCOBQQG3PWJ6V5YMX2L5GR3I/graph.json","fetch_events":"https://pith.science/api/pith-number/HFYCOBQQG3PWJ6V5YMX2L5GR3I/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HFYCOBQQG3PWJ6V5YMX2L5GR3I/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HFYCOBQQG3PWJ6V5YMX2L5GR3I/action/storage_attestation","attest_author":"https://pith.science/pith/HFYCOBQQG3PWJ6V5YMX2L5GR3I/action/author_attestation","sign_citation":"https://pith.science/pith/HFYCOBQQG3PWJ6V5YMX2L5GR3I/action/citation_signature","submit_replication":"https://pith.science/pith/HFYCOBQQG3PWJ6V5YMX2L5GR3I/action/replication_record"}},"created_at":"2026-05-18T03:51:15.304287+00:00","updated_at":"2026-05-18T03:51:15.304287+00:00"}