{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:GGDFX72YTN7RDZYCPDZUKIC6IN","short_pith_number":"pith:GGDFX72Y","schema_version":"1.0","canonical_sha256":"31865bff589b7f11e70278f345205e434299f2b196df8a5a4d4b7be669bdd961","source":{"kind":"arxiv","id":"2305.01843","version":1},"attestation_state":"computed","paper":{"title":"Direct LiDAR-Inertial Odometry and Mapping: Perceptive and Connective SLAM","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Brett T. Lopez, Kenny Chen, Ryan Nemiroff","submitted_at":"2023-05-03T01:06:25Z","abstract_excerpt":"This paper presents Direct LiDAR-Inertial Odometry and Mapping (DLIOM), a robust SLAM algorithm with an explicit focus on computational efficiency, operational reliability, and real-world efficacy. DLIOM contains several key algorithmic innovations in both the front-end and back-end subsystems to design a resilient LiDAR-inertial architecture that is perceptive to the environment and produces accurate localization and high-fidelity 3D mapping for autonomous robotic platforms. Our ideas spawned after a deep investigation into modern LiDAR SLAM systems and their inabilities to generalize across "},"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":"2305.01843","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2023-05-03T01:06:25Z","cross_cats_sorted":[],"title_canon_sha256":"42c8226d1885b3b7269f65f0a214e835edc3f02951ba96612670c942933ad9ae","abstract_canon_sha256":"63b9c0032d27b6588e973b0d0942ab46b50378999dff4517b691de5c6ed73634"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:06:33.161248Z","signature_b64":"gpW4SBGjAmzBhy2sbf7hK+Qz5GRGCuAKyNF1qtXCHzE7pSpZ/EZyiCUoiOG7/Vp86Ue7nT3oBADFDiSKFt9JCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"31865bff589b7f11e70278f345205e434299f2b196df8a5a4d4b7be669bdd961","last_reissued_at":"2026-07-05T06:06:33.160847Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:06:33.160847Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Direct LiDAR-Inertial Odometry and Mapping: Perceptive and Connective SLAM","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Brett T. Lopez, Kenny Chen, Ryan Nemiroff","submitted_at":"2023-05-03T01:06:25Z","abstract_excerpt":"This paper presents Direct LiDAR-Inertial Odometry and Mapping (DLIOM), a robust SLAM algorithm with an explicit focus on computational efficiency, operational reliability, and real-world efficacy. DLIOM contains several key algorithmic innovations in both the front-end and back-end subsystems to design a resilient LiDAR-inertial architecture that is perceptive to the environment and produces accurate localization and high-fidelity 3D mapping for autonomous robotic platforms. Our ideas spawned after a deep investigation into modern LiDAR SLAM systems and their inabilities to generalize across "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.01843","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/2305.01843/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":"2305.01843","created_at":"2026-07-05T06:06:33.160904+00:00"},{"alias_kind":"arxiv_version","alias_value":"2305.01843v1","created_at":"2026-07-05T06:06:33.160904+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.01843","created_at":"2026-07-05T06:06:33.160904+00:00"},{"alias_kind":"pith_short_12","alias_value":"GGDFX72YTN7R","created_at":"2026-07-05T06:06:33.160904+00:00"},{"alias_kind":"pith_short_16","alias_value":"GGDFX72YTN7RDZYC","created_at":"2026-07-05T06:06:33.160904+00:00"},{"alias_kind":"pith_short_8","alias_value":"GGDFX72Y","created_at":"2026-07-05T06:06:33.160904+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.28637","citing_title":"PinNet: Keypoint-Aware Learned Local Descriptors with Geometric Embedding for Loop Closure in LiDAR SLAM","ref_index":20,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/GGDFX72YTN7RDZYCPDZUKIC6IN","json":"https://pith.science/pith/GGDFX72YTN7RDZYCPDZUKIC6IN.json","graph_json":"https://pith.science/api/pith-number/GGDFX72YTN7RDZYCPDZUKIC6IN/graph.json","events_json":"https://pith.science/api/pith-number/GGDFX72YTN7RDZYCPDZUKIC6IN/events.json","paper":"https://pith.science/paper/GGDFX72Y"},"agent_actions":{"view_html":"https://pith.science/pith/GGDFX72YTN7RDZYCPDZUKIC6IN","download_json":"https://pith.science/pith/GGDFX72YTN7RDZYCPDZUKIC6IN.json","view_paper":"https://pith.science/paper/GGDFX72Y","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2305.01843&json=true","fetch_graph":"https://pith.science/api/pith-number/GGDFX72YTN7RDZYCPDZUKIC6IN/graph.json","fetch_events":"https://pith.science/api/pith-number/GGDFX72YTN7RDZYCPDZUKIC6IN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GGDFX72YTN7RDZYCPDZUKIC6IN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GGDFX72YTN7RDZYCPDZUKIC6IN/action/storage_attestation","attest_author":"https://pith.science/pith/GGDFX72YTN7RDZYCPDZUKIC6IN/action/author_attestation","sign_citation":"https://pith.science/pith/GGDFX72YTN7RDZYCPDZUKIC6IN/action/citation_signature","submit_replication":"https://pith.science/pith/GGDFX72YTN7RDZYCPDZUKIC6IN/action/replication_record"}},"created_at":"2026-07-05T06:06:33.160904+00:00","updated_at":"2026-07-05T06:06:33.160904+00:00"}