{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:EDLAMBM7AG5KHXHJ5AF4GNK3LR","short_pith_number":"pith:EDLAMBM7","schema_version":"1.0","canonical_sha256":"20d606059f01baa3dce9e80bc3355b5c7a814bbdb89e2359ed99e51bf064e706","source":{"kind":"arxiv","id":"2602.18164","version":3},"attestation_state":"computed","paper":{"title":"GrandTour: A Legged Robotics Dataset in the Wild for Multi-Modal Perception and State Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Cesar Cadena, Frank Fu, Jonas Frey, Katharine Patterson, Marco Hutter, Maurice Fallon, Tianao Xu, Turcan Tuna","submitted_at":"2026-02-20T11:57:52Z","abstract_excerpt":"Accurate state estimation and multi-modal perception are prerequisites for autonomous legged robots in complex, large-scale environments. To date, no large-scale public legged-robot dataset captures the real-world conditions needed to develop and benchmark algorithms for legged-robot state estimation, perception, and navigation. To address this, we introduce the GrandTour dataset, a multi-modal legged-robotics dataset collected across challenging outdoor and indoor environments, featuring an ANYbotics ANYmal-D quadruped equipped with the Boxi multi-modal sensor payload. GrandTour spans a broad"},"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":"2602.18164","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2026-02-20T11:57:52Z","cross_cats_sorted":[],"title_canon_sha256":"efc812746f90de7e446308daa4ccd0da77b6af1bbaf8d1ccbd5273401e331dcb","abstract_canon_sha256":"a840355cbb8bbecff7e413d36b3af5b907fd199895a6c7b02c006e0cc10fd25a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-09T01:20:35.488536Z","signature_b64":"qcpbbdamB09r235Rby375hvB3vtmNOSDmtqmIP/2AyM1lrVwozKtPx0Yi+1vJ4CWqTIsE+wtUaRj+bFVjqgeDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"20d606059f01baa3dce9e80bc3355b5c7a814bbdb89e2359ed99e51bf064e706","last_reissued_at":"2026-07-09T01:20:35.488023Z","signature_status":"signed_v1","first_computed_at":"2026-07-09T01:20:35.488023Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"GrandTour: A Legged Robotics Dataset in the Wild for Multi-Modal Perception and State Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Cesar Cadena, Frank Fu, Jonas Frey, Katharine Patterson, Marco Hutter, Maurice Fallon, Tianao Xu, Turcan Tuna","submitted_at":"2026-02-20T11:57:52Z","abstract_excerpt":"Accurate state estimation and multi-modal perception are prerequisites for autonomous legged robots in complex, large-scale environments. To date, no large-scale public legged-robot dataset captures the real-world conditions needed to develop and benchmark algorithms for legged-robot state estimation, perception, and navigation. To address this, we introduce the GrandTour dataset, a multi-modal legged-robotics dataset collected across challenging outdoor and indoor environments, featuring an ANYbotics ANYmal-D quadruped equipped with the Boxi multi-modal sensor payload. GrandTour spans a broad"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2602.18164","kind":"arxiv","version":3},"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/2602.18164/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":"2602.18164","created_at":"2026-07-09T01:20:35.488085+00:00"},{"alias_kind":"arxiv_version","alias_value":"2602.18164v3","created_at":"2026-07-09T01:20:35.488085+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2602.18164","created_at":"2026-07-09T01:20:35.488085+00:00"},{"alias_kind":"pith_short_12","alias_value":"EDLAMBM7AG5K","created_at":"2026-07-09T01:20:35.488085+00:00"},{"alias_kind":"pith_short_16","alias_value":"EDLAMBM7AG5KHXHJ","created_at":"2026-07-09T01:20:35.488085+00:00"},{"alias_kind":"pith_short_8","alias_value":"EDLAMBM7","created_at":"2026-07-09T01:20:35.488085+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":9,"internal_anchor_count":9,"sample":[{"citing_arxiv_id":"2607.06782","citing_title":"G-PROBE: Cross-FOV Place Recognition and Certainty-Coupled Localization for 3D Point Clouds","ref_index":57,"is_internal_anchor":true},{"citing_arxiv_id":"2607.06824","citing_title":"CaLiSym: Learning Symplectic Dynamics of Real-World Systems through Structured Canonical Lifts","ref_index":70,"is_internal_anchor":true},{"citing_arxiv_id":"2606.21223","citing_title":"Ultra-Fusion: A Resilient Tightly-Coupled Multi-Sensor Fusion SLAM Framework under Sensor Degradation and Spatiotemporal Perturbation for Intelligent Transportation Systems","ref_index":44,"is_internal_anchor":true},{"citing_arxiv_id":"2606.19675","citing_title":"ForEnt: A Multi-Modal Dataset for Characterizing Quadruped Robot Entrapments in Forest Environments","ref_index":10,"is_internal_anchor":true},{"citing_arxiv_id":"2606.19067","citing_title":"Sensor Configuration Matters: A Systematic Evaluation of Multimodal SLAM on Quadruped Robots","ref_index":12,"is_internal_anchor":true},{"citing_arxiv_id":"2606.15476","citing_title":"FARM: Find Anything using Relational Spatial Memory","ref_index":2,"is_internal_anchor":true},{"citing_arxiv_id":"2605.11674","citing_title":"A Proprioceptive-Only Benchmark for Quadruped State Estimation: ATE, RPE, and Runtime Trade-offs Between Filters and Smoothers","ref_index":4,"is_internal_anchor":true},{"citing_arxiv_id":"2604.15449","citing_title":"Iterated Invariant EKF for Quadruped Robot Odometry","ref_index":44,"is_internal_anchor":true},{"citing_arxiv_id":"2604.19267","citing_title":"Multimodal embodiment-aware navigation transformer","ref_index":28,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/EDLAMBM7AG5KHXHJ5AF4GNK3LR","json":"https://pith.science/pith/EDLAMBM7AG5KHXHJ5AF4GNK3LR.json","graph_json":"https://pith.science/api/pith-number/EDLAMBM7AG5KHXHJ5AF4GNK3LR/graph.json","events_json":"https://pith.science/api/pith-number/EDLAMBM7AG5KHXHJ5AF4GNK3LR/events.json","paper":"https://pith.science/paper/EDLAMBM7"},"agent_actions":{"view_html":"https://pith.science/pith/EDLAMBM7AG5KHXHJ5AF4GNK3LR","download_json":"https://pith.science/pith/EDLAMBM7AG5KHXHJ5AF4GNK3LR.json","view_paper":"https://pith.science/paper/EDLAMBM7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2602.18164&json=true","fetch_graph":"https://pith.science/api/pith-number/EDLAMBM7AG5KHXHJ5AF4GNK3LR/graph.json","fetch_events":"https://pith.science/api/pith-number/EDLAMBM7AG5KHXHJ5AF4GNK3LR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EDLAMBM7AG5KHXHJ5AF4GNK3LR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EDLAMBM7AG5KHXHJ5AF4GNK3LR/action/storage_attestation","attest_author":"https://pith.science/pith/EDLAMBM7AG5KHXHJ5AF4GNK3LR/action/author_attestation","sign_citation":"https://pith.science/pith/EDLAMBM7AG5KHXHJ5AF4GNK3LR/action/citation_signature","submit_replication":"https://pith.science/pith/EDLAMBM7AG5KHXHJ5AF4GNK3LR/action/replication_record"}},"created_at":"2026-07-09T01:20:35.488085+00:00","updated_at":"2026-07-09T01:20:35.488085+00:00"}