{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:HLLOGESMZZ5JR6BIDQB7BNKOBZ","short_pith_number":"pith:HLLOGESM","schema_version":"1.0","canonical_sha256":"3ad6e3124cce7a98f8281c03f0b54e0e58c25f0308be94a1ca636cb70b79b239","source":{"kind":"arxiv","id":"2104.10110","version":2},"attestation_state":"computed","paper":{"title":"Towards Autonomous Robotic Precision Harvesting: Mapping, Localization, Planning and Control for a Legged Tree Harvester","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SY","eess.SY"],"primary_cat":"cs.RO","authors_text":"Dominic Jud, Edo Jelavic, Marco Hutter, Pascal Egli","submitted_at":"2021-04-20T16:38:53Z","abstract_excerpt":"This paper presents an integrated system for performing precision harvesting missions using a legged harvester. Our harvester performs a challenging task of autonomous navigation and tree grabbing in a confined, GPS denied forest environment. Strategies for mapping, localization, planning, and control are proposed and integrated into a fully autonomous system. The mission starts with a human mapping the area of interest using a custom-made sensor module. Subsequently, a human expert selects the trees for harvesting. The sensor module is then mounted on the machine and used for localization wit"},"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":"2104.10110","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2021-04-20T16:38:53Z","cross_cats_sorted":["cs.SY","eess.SY"],"title_canon_sha256":"0aa099722032644ceb7b0ee6bc6be2144223156a710adaf06ed5815f21eb230f","abstract_canon_sha256":"ab6c24f9372324896a2052f9d8d2e4f15ba59137ee35e3253268f8a17f496ef0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:29:15.949554Z","signature_b64":"fx572RaAucjISm1KS1Op1sfN1NlKE3l6qdJB6rDzecCw8eXhRGsIyxJPXJo5xlK7XbAi4EeKqY5rO7W28INNDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3ad6e3124cce7a98f8281c03f0b54e0e58c25f0308be94a1ca636cb70b79b239","last_reissued_at":"2026-07-05T03:29:15.948999Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:29:15.948999Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Towards Autonomous Robotic Precision Harvesting: Mapping, Localization, Planning and Control for a Legged Tree Harvester","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SY","eess.SY"],"primary_cat":"cs.RO","authors_text":"Dominic Jud, Edo Jelavic, Marco Hutter, Pascal Egli","submitted_at":"2021-04-20T16:38:53Z","abstract_excerpt":"This paper presents an integrated system for performing precision harvesting missions using a legged harvester. Our harvester performs a challenging task of autonomous navigation and tree grabbing in a confined, GPS denied forest environment. Strategies for mapping, localization, planning, and control are proposed and integrated into a fully autonomous system. The mission starts with a human mapping the area of interest using a custom-made sensor module. Subsequently, a human expert selects the trees for harvesting. The sensor module is then mounted on the machine and used for localization wit"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2104.10110","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/2104.10110/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":"2104.10110","created_at":"2026-07-05T03:29:15.949088+00:00"},{"alias_kind":"arxiv_version","alias_value":"2104.10110v2","created_at":"2026-07-05T03:29:15.949088+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2104.10110","created_at":"2026-07-05T03:29:15.949088+00:00"},{"alias_kind":"pith_short_12","alias_value":"HLLOGESMZZ5J","created_at":"2026-07-05T03:29:15.949088+00:00"},{"alias_kind":"pith_short_16","alias_value":"HLLOGESMZZ5JR6BI","created_at":"2026-07-05T03:29:15.949088+00:00"},{"alias_kind":"pith_short_8","alias_value":"HLLOGESM","created_at":"2026-07-05T03:29:15.949088+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2501.07360","citing_title":"TimberVision: A Multi-Task Dataset and Framework for Log-Component Segmentation and Tracking in Autonomous Forestry Operations","ref_index":15,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/HLLOGESMZZ5JR6BIDQB7BNKOBZ","json":"https://pith.science/pith/HLLOGESMZZ5JR6BIDQB7BNKOBZ.json","graph_json":"https://pith.science/api/pith-number/HLLOGESMZZ5JR6BIDQB7BNKOBZ/graph.json","events_json":"https://pith.science/api/pith-number/HLLOGESMZZ5JR6BIDQB7BNKOBZ/events.json","paper":"https://pith.science/paper/HLLOGESM"},"agent_actions":{"view_html":"https://pith.science/pith/HLLOGESMZZ5JR6BIDQB7BNKOBZ","download_json":"https://pith.science/pith/HLLOGESMZZ5JR6BIDQB7BNKOBZ.json","view_paper":"https://pith.science/paper/HLLOGESM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2104.10110&json=true","fetch_graph":"https://pith.science/api/pith-number/HLLOGESMZZ5JR6BIDQB7BNKOBZ/graph.json","fetch_events":"https://pith.science/api/pith-number/HLLOGESMZZ5JR6BIDQB7BNKOBZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HLLOGESMZZ5JR6BIDQB7BNKOBZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HLLOGESMZZ5JR6BIDQB7BNKOBZ/action/storage_attestation","attest_author":"https://pith.science/pith/HLLOGESMZZ5JR6BIDQB7BNKOBZ/action/author_attestation","sign_citation":"https://pith.science/pith/HLLOGESMZZ5JR6BIDQB7BNKOBZ/action/citation_signature","submit_replication":"https://pith.science/pith/HLLOGESMZZ5JR6BIDQB7BNKOBZ/action/replication_record"}},"created_at":"2026-07-05T03:29:15.949088+00:00","updated_at":"2026-07-05T03:29:15.949088+00:00"}