{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:FIHP2GBCC23HRB5W6CSU2L3RW7","short_pith_number":"pith:FIHP2GBC","schema_version":"1.0","canonical_sha256":"2a0efd182216b67887b6f0a54d2f71b7fb31c036302e284549184f4da632b602","source":{"kind":"arxiv","id":"1906.04670","version":1},"attestation_state":"computed","paper":{"title":"Automatic Multi-Sensor Extrinsic Calibration for Mobile Robots","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"David Zu\\~niga-No\\\"el, Javier Gonzalez-Jimenez, Jose-Raul Ruiz-Sarmiento, Ruben Gomez-Ojeda","submitted_at":"2019-06-11T15:58:07Z","abstract_excerpt":"In order to fuse measurements from multiple sensors mounted on a mobile robot, it is needed to express them in a common reference system through their relative spatial transformations. In this paper, we present a method to estimate the full 6DoF extrinsic calibration parameters of multiple heterogeneous sensors (Lidars, Depth and RGB cameras) suitable for automatic execution on a mobile robot. Our method computes the 2D calibration parameters (x, y, yaw) through a motion-based approach, while for the remaining 3 parameters (z, pitch, roll) it requires the observation of the ground plane for 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":"1906.04670","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2019-06-11T15:58:07Z","cross_cats_sorted":[],"title_canon_sha256":"701e09b5a17882083afe051d3c7d143939e12c62a697b983d9262650418ae977","abstract_canon_sha256":"7088a813abd9983942632e5998f4de6f84e2e54381096d848a4fa4e46af4830e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:43:33.804568Z","signature_b64":"irWuj2EUyLyWbeynj0Wck35DxbrRP6tx1ml1FCimAz2X6+LceiFfREhvcdYVPej8bjQR/jEIvV7Xyy0SDbNVDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2a0efd182216b67887b6f0a54d2f71b7fb31c036302e284549184f4da632b602","last_reissued_at":"2026-05-17T23:43:33.804090Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:43:33.804090Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Automatic Multi-Sensor Extrinsic Calibration for Mobile Robots","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"David Zu\\~niga-No\\\"el, Javier Gonzalez-Jimenez, Jose-Raul Ruiz-Sarmiento, Ruben Gomez-Ojeda","submitted_at":"2019-06-11T15:58:07Z","abstract_excerpt":"In order to fuse measurements from multiple sensors mounted on a mobile robot, it is needed to express them in a common reference system through their relative spatial transformations. In this paper, we present a method to estimate the full 6DoF extrinsic calibration parameters of multiple heterogeneous sensors (Lidars, Depth and RGB cameras) suitable for automatic execution on a mobile robot. Our method computes the 2D calibration parameters (x, y, yaw) through a motion-based approach, while for the remaining 3 parameters (z, pitch, roll) it requires the observation of the ground plane for a "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.04670","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":"1906.04670","created_at":"2026-05-17T23:43:33.804160+00:00"},{"alias_kind":"arxiv_version","alias_value":"1906.04670v1","created_at":"2026-05-17T23:43:33.804160+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.04670","created_at":"2026-05-17T23:43:33.804160+00:00"},{"alias_kind":"pith_short_12","alias_value":"FIHP2GBCC23H","created_at":"2026-05-18T12:33:15.570797+00:00"},{"alias_kind":"pith_short_16","alias_value":"FIHP2GBCC23HRB5W","created_at":"2026-05-18T12:33:15.570797+00:00"},{"alias_kind":"pith_short_8","alias_value":"FIHP2GBC","created_at":"2026-05-18T12:33:15.570797+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"1907.01839","citing_title":"Intrinsic Calibration of Depth Cameras for Mobile Robots using a Radial Laser Scanner","ref_index":25,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/FIHP2GBCC23HRB5W6CSU2L3RW7","json":"https://pith.science/pith/FIHP2GBCC23HRB5W6CSU2L3RW7.json","graph_json":"https://pith.science/api/pith-number/FIHP2GBCC23HRB5W6CSU2L3RW7/graph.json","events_json":"https://pith.science/api/pith-number/FIHP2GBCC23HRB5W6CSU2L3RW7/events.json","paper":"https://pith.science/paper/FIHP2GBC"},"agent_actions":{"view_html":"https://pith.science/pith/FIHP2GBCC23HRB5W6CSU2L3RW7","download_json":"https://pith.science/pith/FIHP2GBCC23HRB5W6CSU2L3RW7.json","view_paper":"https://pith.science/paper/FIHP2GBC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1906.04670&json=true","fetch_graph":"https://pith.science/api/pith-number/FIHP2GBCC23HRB5W6CSU2L3RW7/graph.json","fetch_events":"https://pith.science/api/pith-number/FIHP2GBCC23HRB5W6CSU2L3RW7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FIHP2GBCC23HRB5W6CSU2L3RW7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FIHP2GBCC23HRB5W6CSU2L3RW7/action/storage_attestation","attest_author":"https://pith.science/pith/FIHP2GBCC23HRB5W6CSU2L3RW7/action/author_attestation","sign_citation":"https://pith.science/pith/FIHP2GBCC23HRB5W6CSU2L3RW7/action/citation_signature","submit_replication":"https://pith.science/pith/FIHP2GBCC23HRB5W6CSU2L3RW7/action/replication_record"}},"created_at":"2026-05-17T23:43:33.804160+00:00","updated_at":"2026-05-17T23:43:33.804160+00:00"}