{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:XQBSR3PVKTJPL6KXN6226PKFJE","short_pith_number":"pith:XQBSR3PV","schema_version":"1.0","canonical_sha256":"bc0328edf554d2f5f9576fb5af3d45491ad2765e62201e5d150f257f122836c9","source":{"kind":"arxiv","id":"2406.16850","version":1},"attestation_state":"computed","paper":{"title":"From Perfect to Noisy World Simulation: Customizable Embodied Multi-modal Perturbations for SLAM Robustness Benchmarking","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.CV","authors_text":"Bhiksha Raj, Matthew Johnson-Roberson, Sibo Wang, Tianyi Zhang, Xiang Li, Xiaohao Xu, Xiaonan Huang, Ye Li, Yongqi Chen","submitted_at":"2024-06-24T17:57:05Z","abstract_excerpt":"Embodied agents require robust navigation systems to operate in unstructured environments, making the robustness of Simultaneous Localization and Mapping (SLAM) models critical to embodied agent autonomy. While real-world datasets are invaluable, simulation-based benchmarks offer a scalable approach for robustness evaluations. However, the creation of a challenging and controllable noisy world with diverse perturbations remains under-explored. To this end, we propose a novel, customizable pipeline for noisy data synthesis, aimed at assessing the resilience of multi-modal SLAM models against va"},"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":"2406.16850","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-06-24T17:57:05Z","cross_cats_sorted":["cs.RO"],"title_canon_sha256":"581befd0c13142cedbf1d3aec968a8fa01504dd006cab6e2d80a0aa14596255c","abstract_canon_sha256":"62ea2d63216ccab13200eb33479ef3b6b19b4cfc60fa4b8686a6f2a7d83a2e78"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:36:04.551893Z","signature_b64":"RBkTZf/W5KEqO7Sd+/sJmuDvG2j8xwCp2m6Md+U6nbPIcAvNudt3Nf+/TUQHsiM2Zhgh2B8UkMQ0WKXASyDeAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bc0328edf554d2f5f9576fb5af3d45491ad2765e62201e5d150f257f122836c9","last_reissued_at":"2026-07-05T08:36:04.551452Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:36:04.551452Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"From Perfect to Noisy World Simulation: Customizable Embodied Multi-modal Perturbations for SLAM Robustness Benchmarking","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.CV","authors_text":"Bhiksha Raj, Matthew Johnson-Roberson, Sibo Wang, Tianyi Zhang, Xiang Li, Xiaohao Xu, Xiaonan Huang, Ye Li, Yongqi Chen","submitted_at":"2024-06-24T17:57:05Z","abstract_excerpt":"Embodied agents require robust navigation systems to operate in unstructured environments, making the robustness of Simultaneous Localization and Mapping (SLAM) models critical to embodied agent autonomy. While real-world datasets are invaluable, simulation-based benchmarks offer a scalable approach for robustness evaluations. However, the creation of a challenging and controllable noisy world with diverse perturbations remains under-explored. To this end, we propose a novel, customizable pipeline for noisy data synthesis, aimed at assessing the resilience of multi-modal SLAM models against va"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.16850","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/2406.16850/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":"2406.16850","created_at":"2026-07-05T08:36:04.551511+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.16850v1","created_at":"2026-07-05T08:36:04.551511+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.16850","created_at":"2026-07-05T08:36:04.551511+00:00"},{"alias_kind":"pith_short_12","alias_value":"XQBSR3PVKTJP","created_at":"2026-07-05T08:36:04.551511+00:00"},{"alias_kind":"pith_short_16","alias_value":"XQBSR3PVKTJPL6KX","created_at":"2026-07-05T08:36:04.551511+00:00"},{"alias_kind":"pith_short_8","alias_value":"XQBSR3PV","created_at":"2026-07-05T08:36:04.551511+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.03263","citing_title":"NeRF and Gaussian Splatting SLAM in the Wild","ref_index":17,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/XQBSR3PVKTJPL6KXN6226PKFJE","json":"https://pith.science/pith/XQBSR3PVKTJPL6KXN6226PKFJE.json","graph_json":"https://pith.science/api/pith-number/XQBSR3PVKTJPL6KXN6226PKFJE/graph.json","events_json":"https://pith.science/api/pith-number/XQBSR3PVKTJPL6KXN6226PKFJE/events.json","paper":"https://pith.science/paper/XQBSR3PV"},"agent_actions":{"view_html":"https://pith.science/pith/XQBSR3PVKTJPL6KXN6226PKFJE","download_json":"https://pith.science/pith/XQBSR3PVKTJPL6KXN6226PKFJE.json","view_paper":"https://pith.science/paper/XQBSR3PV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.16850&json=true","fetch_graph":"https://pith.science/api/pith-number/XQBSR3PVKTJPL6KXN6226PKFJE/graph.json","fetch_events":"https://pith.science/api/pith-number/XQBSR3PVKTJPL6KXN6226PKFJE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XQBSR3PVKTJPL6KXN6226PKFJE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XQBSR3PVKTJPL6KXN6226PKFJE/action/storage_attestation","attest_author":"https://pith.science/pith/XQBSR3PVKTJPL6KXN6226PKFJE/action/author_attestation","sign_citation":"https://pith.science/pith/XQBSR3PVKTJPL6KXN6226PKFJE/action/citation_signature","submit_replication":"https://pith.science/pith/XQBSR3PVKTJPL6KXN6226PKFJE/action/replication_record"}},"created_at":"2026-07-05T08:36:04.551511+00:00","updated_at":"2026-07-05T08:36:04.551511+00:00"}