{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:ZLO2RV4AULKPLIHKQPAY54UEMJ","short_pith_number":"pith:ZLO2RV4A","schema_version":"1.0","canonical_sha256":"cadda8d780a2d4f5a0ea83c18ef28462454bca627b59b60c78b6bad172b7d30e","source":{"kind":"arxiv","id":"2207.10782","version":2},"attestation_state":"computed","paper":{"title":"Learning Deep SDF Maps Online for Robot Navigation and Exploration","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Gadiel Sznaier Camps, Mac Schwager, Marco Pavone, Robert Dyro","submitted_at":"2022-07-21T23:03:51Z","abstract_excerpt":"We propose an algorithm to (i) learn online a deep signed distance function (SDF) with a LiDAR-equipped robot to represent the 3D environment geometry, and (ii) plan collision-free trajectories given this deep learned map. Our algorithm takes a stream of incoming LiDAR scans and continually optimizes a neural network to represent the SDF of the environment around its current vicinity. When the SDF network quality saturates, we cache a copy of the network, along with a learned confidence metric, and initialize a new SDF network to continue mapping new regions of the environment. We then concate"},"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":"2207.10782","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.RO","submitted_at":"2022-07-21T23:03:51Z","cross_cats_sorted":[],"title_canon_sha256":"da02f0f6b43764fd0c5f224400f368331851b23192b41f4b9b7f04d2c5a75c19","abstract_canon_sha256":"4109e5764b17c25af5f5aba96d096948879eee385e33bd99f5290f75e28daa83"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:45:38.244882Z","signature_b64":"CzbPovbqy1i8QKsL5JGUpgPYLBJziviQuLYB99xC8GWoYgr6n1upyQNZhDeoccDL8HoaWfopfT1AbY2NFpWLAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cadda8d780a2d4f5a0ea83c18ef28462454bca627b59b60c78b6bad172b7d30e","last_reissued_at":"2026-07-05T04:45:38.244468Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:45:38.244468Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning Deep SDF Maps Online for Robot Navigation and Exploration","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Gadiel Sznaier Camps, Mac Schwager, Marco Pavone, Robert Dyro","submitted_at":"2022-07-21T23:03:51Z","abstract_excerpt":"We propose an algorithm to (i) learn online a deep signed distance function (SDF) with a LiDAR-equipped robot to represent the 3D environment geometry, and (ii) plan collision-free trajectories given this deep learned map. Our algorithm takes a stream of incoming LiDAR scans and continually optimizes a neural network to represent the SDF of the environment around its current vicinity. When the SDF network quality saturates, we cache a copy of the network, along with a learned confidence metric, and initialize a new SDF network to continue mapping new regions of the environment. We then concate"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2207.10782","kind":"arxiv","version":2},"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/2207.10782/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":"2207.10782","created_at":"2026-07-05T04:45:38.244522+00:00"},{"alias_kind":"arxiv_version","alias_value":"2207.10782v2","created_at":"2026-07-05T04:45:38.244522+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2207.10782","created_at":"2026-07-05T04:45:38.244522+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZLO2RV4AULKP","created_at":"2026-07-05T04:45:38.244522+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZLO2RV4AULKPLIHK","created_at":"2026-07-05T04:45:38.244522+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZLO2RV4A","created_at":"2026-07-05T04:45:38.244522+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2409.01652","citing_title":"ReKep: Spatio-Temporal Reasoning of Relational Keypoint Constraints for Robotic Manipulation","ref_index":89,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZLO2RV4AULKPLIHKQPAY54UEMJ","json":"https://pith.science/pith/ZLO2RV4AULKPLIHKQPAY54UEMJ.json","graph_json":"https://pith.science/api/pith-number/ZLO2RV4AULKPLIHKQPAY54UEMJ/graph.json","events_json":"https://pith.science/api/pith-number/ZLO2RV4AULKPLIHKQPAY54UEMJ/events.json","paper":"https://pith.science/paper/ZLO2RV4A"},"agent_actions":{"view_html":"https://pith.science/pith/ZLO2RV4AULKPLIHKQPAY54UEMJ","download_json":"https://pith.science/pith/ZLO2RV4AULKPLIHKQPAY54UEMJ.json","view_paper":"https://pith.science/paper/ZLO2RV4A","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2207.10782&json=true","fetch_graph":"https://pith.science/api/pith-number/ZLO2RV4AULKPLIHKQPAY54UEMJ/graph.json","fetch_events":"https://pith.science/api/pith-number/ZLO2RV4AULKPLIHKQPAY54UEMJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZLO2RV4AULKPLIHKQPAY54UEMJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZLO2RV4AULKPLIHKQPAY54UEMJ/action/storage_attestation","attest_author":"https://pith.science/pith/ZLO2RV4AULKPLIHKQPAY54UEMJ/action/author_attestation","sign_citation":"https://pith.science/pith/ZLO2RV4AULKPLIHKQPAY54UEMJ/action/citation_signature","submit_replication":"https://pith.science/pith/ZLO2RV4AULKPLIHKQPAY54UEMJ/action/replication_record"}},"created_at":"2026-07-05T04:45:38.244522+00:00","updated_at":"2026-07-05T04:45:38.244522+00:00"}