{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:2TZAO5Q3IIHFIUSMQL5SOQAREG","short_pith_number":"pith:2TZAO5Q3","schema_version":"1.0","canonical_sha256":"d4f207761b420e54524c82fb27401121bda65d99e50f898ab90cf32dd3c03691","source":{"kind":"arxiv","id":"2203.01389","version":1},"attestation_state":"computed","paper":{"title":"Graph-based Multi-sensor Fusion for Consistent Localization of Autonomous Construction Robots","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SY","eess.SY"],"primary_cat":"cs.RO","authors_text":"Julian Nubert, Marco Hutter, Shehryar Khattak","submitted_at":"2022-03-02T20:19:54Z","abstract_excerpt":"Enabling autonomous operation of large-scale construction machines, such as excavators, can bring key benefits for human safety and operational opportunities for applications in dangerous and hazardous environments. To facilitate robot autonomy, robust and accurate state-estimation remains a core component to enable these machines for operation in a diverse set of complex environments. In this work, a method for multi-modal sensor fusion for robot state-estimation and localization is presented, enabling operation of construction robots in real-world scenarios. The proposed approach presents a "},"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":"2203.01389","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2022-03-02T20:19:54Z","cross_cats_sorted":["cs.SY","eess.SY"],"title_canon_sha256":"4bb6da8e5563ff03af3eb81f4a4eee437205e42341b220da7cb4b8b79cb36ed7","abstract_canon_sha256":"d235c0f1696a5316ad9a94946797bbdb8fdeefd109cf1c39db045b8db6b7f7c4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:01:47.517350Z","signature_b64":"nxghXQKWyDpsLr0op1Ttd/iTlTOfZtgOCTHBiL9T/wih+uMa6xvSBhU7BTyD8Bjbmv79uSeEOSSOIgr5M9k4Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d4f207761b420e54524c82fb27401121bda65d99e50f898ab90cf32dd3c03691","last_reissued_at":"2026-07-05T04:01:47.516961Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:01:47.516961Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Graph-based Multi-sensor Fusion for Consistent Localization of Autonomous Construction Robots","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SY","eess.SY"],"primary_cat":"cs.RO","authors_text":"Julian Nubert, Marco Hutter, Shehryar Khattak","submitted_at":"2022-03-02T20:19:54Z","abstract_excerpt":"Enabling autonomous operation of large-scale construction machines, such as excavators, can bring key benefits for human safety and operational opportunities for applications in dangerous and hazardous environments. To facilitate robot autonomy, robust and accurate state-estimation remains a core component to enable these machines for operation in a diverse set of complex environments. In this work, a method for multi-modal sensor fusion for robot state-estimation and localization is presented, enabling operation of construction robots in real-world scenarios. The proposed approach presents a "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.01389","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/2203.01389/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":"2203.01389","created_at":"2026-07-05T04:01:47.517016+00:00"},{"alias_kind":"arxiv_version","alias_value":"2203.01389v1","created_at":"2026-07-05T04:01:47.517016+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.01389","created_at":"2026-07-05T04:01:47.517016+00:00"},{"alias_kind":"pith_short_12","alias_value":"2TZAO5Q3IIHF","created_at":"2026-07-05T04:01:47.517016+00:00"},{"alias_kind":"pith_short_16","alias_value":"2TZAO5Q3IIHFIUSM","created_at":"2026-07-05T04:01:47.517016+00:00"},{"alias_kind":"pith_short_8","alias_value":"2TZAO5Q3","created_at":"2026-07-05T04:01:47.517016+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2508.05991","citing_title":"ECMF: Enhanced Cross-Modal Fusion for Multimodal Emotion Recognition in MER-SEMI Challenge","ref_index":9,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/2TZAO5Q3IIHFIUSMQL5SOQAREG","json":"https://pith.science/pith/2TZAO5Q3IIHFIUSMQL5SOQAREG.json","graph_json":"https://pith.science/api/pith-number/2TZAO5Q3IIHFIUSMQL5SOQAREG/graph.json","events_json":"https://pith.science/api/pith-number/2TZAO5Q3IIHFIUSMQL5SOQAREG/events.json","paper":"https://pith.science/paper/2TZAO5Q3"},"agent_actions":{"view_html":"https://pith.science/pith/2TZAO5Q3IIHFIUSMQL5SOQAREG","download_json":"https://pith.science/pith/2TZAO5Q3IIHFIUSMQL5SOQAREG.json","view_paper":"https://pith.science/paper/2TZAO5Q3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2203.01389&json=true","fetch_graph":"https://pith.science/api/pith-number/2TZAO5Q3IIHFIUSMQL5SOQAREG/graph.json","fetch_events":"https://pith.science/api/pith-number/2TZAO5Q3IIHFIUSMQL5SOQAREG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2TZAO5Q3IIHFIUSMQL5SOQAREG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2TZAO5Q3IIHFIUSMQL5SOQAREG/action/storage_attestation","attest_author":"https://pith.science/pith/2TZAO5Q3IIHFIUSMQL5SOQAREG/action/author_attestation","sign_citation":"https://pith.science/pith/2TZAO5Q3IIHFIUSMQL5SOQAREG/action/citation_signature","submit_replication":"https://pith.science/pith/2TZAO5Q3IIHFIUSMQL5SOQAREG/action/replication_record"}},"created_at":"2026-07-05T04:01:47.517016+00:00","updated_at":"2026-07-05T04:01:47.517016+00:00"}