{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:G3UC7TRRYJODUTNRQNHEWQMSLD","short_pith_number":"pith:G3UC7TRR","schema_version":"1.0","canonical_sha256":"36e82fce31c25c3a4db1834e4b419258c686958878841228a6abd29232030ae7","source":{"kind":"arxiv","id":"2308.02799","version":1},"attestation_state":"computed","paper":{"title":"VoxelMap++: Mergeable Voxel Mapping Method for Online LiDAR(-inertial) Odometry","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Chang Wu, Qiyan Li, Xiaotong Kong, Yifei Yuan, Ying Zhang, Yuan You","submitted_at":"2023-08-05T06:07:16Z","abstract_excerpt":"This paper presents VoxelMap++: a voxel mapping method with plane merging which can effectively improve the accuracy and efficiency of LiDAR(-inertial) based simultaneous localization and mapping (SLAM). This map is a collection of voxels that contains one plane feature with 3DOF representation and corresponding covariance estimation. Considering total map will contain a large number of coplanar features (kid planes), these kid planes' 3DOF estimation can be regarded as the measurements with covariance of a larger plane (father plane). Thus, we design a plane merging module based on union-find"},"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":"2308.02799","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2023-08-05T06:07:16Z","cross_cats_sorted":[],"title_canon_sha256":"fd9b7cce40672c9158e8dcb51b608c8b75d02d7a9e4b062c7d2f462cb82331c2","abstract_canon_sha256":"341bd1812ed984fa68f3f9692c0b54410436ed15048ad2c15bec470100a2a3d7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:38:07.704529Z","signature_b64":"G+h5J7ttNywKbK3wM3+tBPmDMgseOCAkA3HOgV4C02Z/AE5zuo8KRo12CWjXfFphAUlsy4ZMfVd4g7NNcOSyBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"36e82fce31c25c3a4db1834e4b419258c686958878841228a6abd29232030ae7","last_reissued_at":"2026-07-05T06:38:07.704065Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:38:07.704065Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"VoxelMap++: Mergeable Voxel Mapping Method for Online LiDAR(-inertial) Odometry","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Chang Wu, Qiyan Li, Xiaotong Kong, Yifei Yuan, Ying Zhang, Yuan You","submitted_at":"2023-08-05T06:07:16Z","abstract_excerpt":"This paper presents VoxelMap++: a voxel mapping method with plane merging which can effectively improve the accuracy and efficiency of LiDAR(-inertial) based simultaneous localization and mapping (SLAM). This map is a collection of voxels that contains one plane feature with 3DOF representation and corresponding covariance estimation. Considering total map will contain a large number of coplanar features (kid planes), these kid planes' 3DOF estimation can be regarded as the measurements with covariance of a larger plane (father plane). Thus, we design a plane merging module based on union-find"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.02799","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/2308.02799/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":"2308.02799","created_at":"2026-07-05T06:38:07.704121+00:00"},{"alias_kind":"arxiv_version","alias_value":"2308.02799v1","created_at":"2026-07-05T06:38:07.704121+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.02799","created_at":"2026-07-05T06:38:07.704121+00:00"},{"alias_kind":"pith_short_12","alias_value":"G3UC7TRRYJOD","created_at":"2026-07-05T06:38:07.704121+00:00"},{"alias_kind":"pith_short_16","alias_value":"G3UC7TRRYJODUTNR","created_at":"2026-07-05T06:38:07.704121+00:00"},{"alias_kind":"pith_short_8","alias_value":"G3UC7TRR","created_at":"2026-07-05T06:38:07.704121+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.24495","citing_title":"Elevator-LIO: Robust LiDAR-Inertial Odometry for Multi-Floor Navigation under Elevator-Induced Non-Inertial Motion","ref_index":20,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/G3UC7TRRYJODUTNRQNHEWQMSLD","json":"https://pith.science/pith/G3UC7TRRYJODUTNRQNHEWQMSLD.json","graph_json":"https://pith.science/api/pith-number/G3UC7TRRYJODUTNRQNHEWQMSLD/graph.json","events_json":"https://pith.science/api/pith-number/G3UC7TRRYJODUTNRQNHEWQMSLD/events.json","paper":"https://pith.science/paper/G3UC7TRR"},"agent_actions":{"view_html":"https://pith.science/pith/G3UC7TRRYJODUTNRQNHEWQMSLD","download_json":"https://pith.science/pith/G3UC7TRRYJODUTNRQNHEWQMSLD.json","view_paper":"https://pith.science/paper/G3UC7TRR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2308.02799&json=true","fetch_graph":"https://pith.science/api/pith-number/G3UC7TRRYJODUTNRQNHEWQMSLD/graph.json","fetch_events":"https://pith.science/api/pith-number/G3UC7TRRYJODUTNRQNHEWQMSLD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/G3UC7TRRYJODUTNRQNHEWQMSLD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/G3UC7TRRYJODUTNRQNHEWQMSLD/action/storage_attestation","attest_author":"https://pith.science/pith/G3UC7TRRYJODUTNRQNHEWQMSLD/action/author_attestation","sign_citation":"https://pith.science/pith/G3UC7TRRYJODUTNRQNHEWQMSLD/action/citation_signature","submit_replication":"https://pith.science/pith/G3UC7TRRYJODUTNRQNHEWQMSLD/action/replication_record"}},"created_at":"2026-07-05T06:38:07.704121+00:00","updated_at":"2026-07-05T06:38:07.704121+00:00"}