{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:FZCWOFIAGHBMXGEY5G6RZOD37F","short_pith_number":"pith:FZCWOFIA","schema_version":"1.0","canonical_sha256":"2e4567150031c2cb9898e9bd1cb87bf95f38f9af496ae33d04bf847b383de27f","source":{"kind":"arxiv","id":"2606.26928","version":1},"attestation_state":"computed","paper":{"title":"UAV-MapFusion: RTK-Aligned Uncertainty-Aware Coarse-to-Fine Multi-Session UAV Mapping","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SI"],"primary_cat":"cs.RO","authors_text":"Bing Xue, Chunran Zheng, Feng Pan, Jiayu Wen, Wei Wang, Yukang Cui, Zhiyu Chen","submitted_at":"2026-06-25T12:03:29Z","abstract_excerpt":"Large-scale point cloud maps are essential for robotics and spatial intelligence tasks. UAVs provide an efficient means for large-scale map acquisition; however, due to limited flight endurance and onboard storage, mapping a large-scale scene within a single flight remains difficult. Existing multi-session map merging methods can extend the mapping range, yet in UAV scenarios they still struggle to simultaneously suppress long-range drift and preserve local geometric accuracy. To address this issue, an uncertainty-aware multi-session point cloud map merging and coarse-to-fine optimization syst"},"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":"2606.26928","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2026-06-25T12:03:29Z","cross_cats_sorted":["cs.SI"],"title_canon_sha256":"76f489ab68e2bf1e87e8833ed462f69310e7220a2197c0970b03cefa4cf8ae59","abstract_canon_sha256":"523e3167372df40dedcd7ed51c718eb6333c3dce04afab2d4e9acbe967a9eb47"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-26T01:16:04.357430Z","signature_b64":"olyd0iIPFNhgw2L8sBQXuzLrt2rJpldVyaOo5oxOWEYQ9uRp8iswth6OAgfF6KJVPCXWjDKph7dwg900lgveBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2e4567150031c2cb9898e9bd1cb87bf95f38f9af496ae33d04bf847b383de27f","last_reissued_at":"2026-06-26T01:16:04.357027Z","signature_status":"signed_v1","first_computed_at":"2026-06-26T01:16:04.357027Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"UAV-MapFusion: RTK-Aligned Uncertainty-Aware Coarse-to-Fine Multi-Session UAV Mapping","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SI"],"primary_cat":"cs.RO","authors_text":"Bing Xue, Chunran Zheng, Feng Pan, Jiayu Wen, Wei Wang, Yukang Cui, Zhiyu Chen","submitted_at":"2026-06-25T12:03:29Z","abstract_excerpt":"Large-scale point cloud maps are essential for robotics and spatial intelligence tasks. UAVs provide an efficient means for large-scale map acquisition; however, due to limited flight endurance and onboard storage, mapping a large-scale scene within a single flight remains difficult. Existing multi-session map merging methods can extend the mapping range, yet in UAV scenarios they still struggle to simultaneously suppress long-range drift and preserve local geometric accuracy. To address this issue, an uncertainty-aware multi-session point cloud map merging and coarse-to-fine optimization syst"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.26928","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/2606.26928/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":"2606.26928","created_at":"2026-06-26T01:16:04.357082+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.26928v1","created_at":"2026-06-26T01:16:04.357082+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.26928","created_at":"2026-06-26T01:16:04.357082+00:00"},{"alias_kind":"pith_short_12","alias_value":"FZCWOFIAGHBM","created_at":"2026-06-26T01:16:04.357082+00:00"},{"alias_kind":"pith_short_16","alias_value":"FZCWOFIAGHBMXGEY","created_at":"2026-06-26T01:16:04.357082+00:00"},{"alias_kind":"pith_short_8","alias_value":"FZCWOFIA","created_at":"2026-06-26T01:16:04.357082+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/FZCWOFIAGHBMXGEY5G6RZOD37F","json":"https://pith.science/pith/FZCWOFIAGHBMXGEY5G6RZOD37F.json","graph_json":"https://pith.science/api/pith-number/FZCWOFIAGHBMXGEY5G6RZOD37F/graph.json","events_json":"https://pith.science/api/pith-number/FZCWOFIAGHBMXGEY5G6RZOD37F/events.json","paper":"https://pith.science/paper/FZCWOFIA"},"agent_actions":{"view_html":"https://pith.science/pith/FZCWOFIAGHBMXGEY5G6RZOD37F","download_json":"https://pith.science/pith/FZCWOFIAGHBMXGEY5G6RZOD37F.json","view_paper":"https://pith.science/paper/FZCWOFIA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.26928&json=true","fetch_graph":"https://pith.science/api/pith-number/FZCWOFIAGHBMXGEY5G6RZOD37F/graph.json","fetch_events":"https://pith.science/api/pith-number/FZCWOFIAGHBMXGEY5G6RZOD37F/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FZCWOFIAGHBMXGEY5G6RZOD37F/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FZCWOFIAGHBMXGEY5G6RZOD37F/action/storage_attestation","attest_author":"https://pith.science/pith/FZCWOFIAGHBMXGEY5G6RZOD37F/action/author_attestation","sign_citation":"https://pith.science/pith/FZCWOFIAGHBMXGEY5G6RZOD37F/action/citation_signature","submit_replication":"https://pith.science/pith/FZCWOFIAGHBMXGEY5G6RZOD37F/action/replication_record"}},"created_at":"2026-06-26T01:16:04.357082+00:00","updated_at":"2026-06-26T01:16:04.357082+00:00"}