{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:SV22CQKYOG7SQCK573OSW46VTJ","short_pith_number":"pith:SV22CQKY","schema_version":"1.0","canonical_sha256":"9575a1415871bf28095dfedd2b73d59a4d24fe18235c5a83941ab5728ec8e41a","source":{"kind":"arxiv","id":"2405.01918","version":1},"attestation_state":"computed","paper":{"title":"An Onboard Framework for Staircases Modeling Based on Point Clouds","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Chun Qing, Gan Ma, Rongxiang Zeng, Xuan Wu, Yongliang Shi","submitted_at":"2024-05-03T08:18:06Z","abstract_excerpt":"The detection of traversable regions on staircases and the physical modeling constitutes pivotal aspects of the mobility of legged robots. This paper presents an onboard framework tailored to the detection of traversable regions and the modeling of physical attributes of staircases by point cloud data. To mitigate the influence of illumination variations and the overfitting due to the dataset diversity, a series of data augmentations are introduced to enhance the training of the fundamental network. A curvature suppression cross-entropy(CSCE) loss is proposed to reduce the ambiguity of predict"},"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":"2405.01918","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2024-05-03T08:18:06Z","cross_cats_sorted":[],"title_canon_sha256":"035e579fadce3fb2049bd6a58b422508be5dbc492800708da21a912117bcf8dd","abstract_canon_sha256":"43a55637f52eb2952695326e9c13f26ef0c1284b909f967214d8ad6c510ca7ed"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:15:04.296258Z","signature_b64":"n4T0q2i5BXezLbPbu13h255sTBtQwSuLsd1/2wB3+SdTQbeQfIg9UYBVWv3feqqcZIt89cPIALIQZfRSilrGDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9575a1415871bf28095dfedd2b73d59a4d24fe18235c5a83941ab5728ec8e41a","last_reissued_at":"2026-07-05T08:15:04.295780Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:15:04.295780Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"An Onboard Framework for Staircases Modeling Based on Point Clouds","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Chun Qing, Gan Ma, Rongxiang Zeng, Xuan Wu, Yongliang Shi","submitted_at":"2024-05-03T08:18:06Z","abstract_excerpt":"The detection of traversable regions on staircases and the physical modeling constitutes pivotal aspects of the mobility of legged robots. This paper presents an onboard framework tailored to the detection of traversable regions and the modeling of physical attributes of staircases by point cloud data. To mitigate the influence of illumination variations and the overfitting due to the dataset diversity, a series of data augmentations are introduced to enhance the training of the fundamental network. A curvature suppression cross-entropy(CSCE) loss is proposed to reduce the ambiguity of predict"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.01918","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/2405.01918/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":"2405.01918","created_at":"2026-07-05T08:15:04.295842+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.01918v1","created_at":"2026-07-05T08:15:04.295842+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.01918","created_at":"2026-07-05T08:15:04.295842+00:00"},{"alias_kind":"pith_short_12","alias_value":"SV22CQKYOG7S","created_at":"2026-07-05T08:15:04.295842+00:00"},{"alias_kind":"pith_short_16","alias_value":"SV22CQKYOG7SQCK5","created_at":"2026-07-05T08:15:04.295842+00:00"},{"alias_kind":"pith_short_8","alias_value":"SV22CQKY","created_at":"2026-07-05T08:15:04.295842+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2501.04170","citing_title":"A Bayesian Modeling Framework for Estimation and Ground Segmentation of Cluttered Staircases","ref_index":7,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SV22CQKYOG7SQCK573OSW46VTJ","json":"https://pith.science/pith/SV22CQKYOG7SQCK573OSW46VTJ.json","graph_json":"https://pith.science/api/pith-number/SV22CQKYOG7SQCK573OSW46VTJ/graph.json","events_json":"https://pith.science/api/pith-number/SV22CQKYOG7SQCK573OSW46VTJ/events.json","paper":"https://pith.science/paper/SV22CQKY"},"agent_actions":{"view_html":"https://pith.science/pith/SV22CQKYOG7SQCK573OSW46VTJ","download_json":"https://pith.science/pith/SV22CQKYOG7SQCK573OSW46VTJ.json","view_paper":"https://pith.science/paper/SV22CQKY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.01918&json=true","fetch_graph":"https://pith.science/api/pith-number/SV22CQKYOG7SQCK573OSW46VTJ/graph.json","fetch_events":"https://pith.science/api/pith-number/SV22CQKYOG7SQCK573OSW46VTJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SV22CQKYOG7SQCK573OSW46VTJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SV22CQKYOG7SQCK573OSW46VTJ/action/storage_attestation","attest_author":"https://pith.science/pith/SV22CQKYOG7SQCK573OSW46VTJ/action/author_attestation","sign_citation":"https://pith.science/pith/SV22CQKYOG7SQCK573OSW46VTJ/action/citation_signature","submit_replication":"https://pith.science/pith/SV22CQKYOG7SQCK573OSW46VTJ/action/replication_record"}},"created_at":"2026-07-05T08:15:04.295842+00:00","updated_at":"2026-07-05T08:15:04.295842+00:00"}