{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:LKBVNNOAFWOPVOSS2T6QYIXKJY","short_pith_number":"pith:LKBVNNOA","canonical_record":{"source":{"id":"1810.12465","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2018-10-30T00:33:51Z","cross_cats_sorted":[],"title_canon_sha256":"e60a8772fa689a4e0a8f054959bc189fc7d66eaea2af06130c457ff3843ffcd7","abstract_canon_sha256":"e97481e148e69773682cfcd8fdd338d908acc484204412d17416ef39c097fea9"},"schema_version":"1.0"},"canonical_sha256":"5a8356b5c02d9cfaba52d4fd0c22ea4e18eca17832b7c8a8cc67f8c3adb536ee","source":{"kind":"arxiv","id":"1810.12465","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1810.12465","created_at":"2026-05-18T00:02:02Z"},{"alias_kind":"arxiv_version","alias_value":"1810.12465v1","created_at":"2026-05-18T00:02:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.12465","created_at":"2026-05-18T00:02:02Z"},{"alias_kind":"pith_short_12","alias_value":"LKBVNNOAFWOP","created_at":"2026-05-18T12:32:37Z"},{"alias_kind":"pith_short_16","alias_value":"LKBVNNOAFWOPVOSS","created_at":"2026-05-18T12:32:37Z"},{"alias_kind":"pith_short_8","alias_value":"LKBVNNOA","created_at":"2026-05-18T12:32:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:LKBVNNOAFWOPVOSS2T6QYIXKJY","target":"record","payload":{"canonical_record":{"source":{"id":"1810.12465","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2018-10-30T00:33:51Z","cross_cats_sorted":[],"title_canon_sha256":"e60a8772fa689a4e0a8f054959bc189fc7d66eaea2af06130c457ff3843ffcd7","abstract_canon_sha256":"e97481e148e69773682cfcd8fdd338d908acc484204412d17416ef39c097fea9"},"schema_version":"1.0"},"canonical_sha256":"5a8356b5c02d9cfaba52d4fd0c22ea4e18eca17832b7c8a8cc67f8c3adb536ee","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:02:02.619653Z","signature_b64":"TMEZy9qmpRleOCN0QBCZ+P1VINmc+i5rWJxadwzlign3XNe4GOAoq3Cpr/Q2avMl1MR3AVS33so03s6KvS/TAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5a8356b5c02d9cfaba52d4fd0c22ea4e18eca17832b7c8a8cc67f8c3adb536ee","last_reissued_at":"2026-05-18T00:02:02.619161Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:02:02.619161Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1810.12465","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:02:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4zl18J/t5OdzJ2EHl7p22uT0HGZlkWWd05GDade8XpV5aPSFgA/LN8gIQDLC1vyOyNZvBDtJA86Rp5lLsP/zBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-29T01:54:22.205676Z"},"content_sha256":"5625b6d0a9b9aa33fba01408ddfef638f12e384b032c156ac5627dc0301c6d68","schema_version":"1.0","event_id":"sha256:5625b6d0a9b9aa33fba01408ddfef638f12e384b032c156ac5627dc0301c6d68"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:LKBVNNOAFWOPVOSS2T6QYIXKJY","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Feature Map Filtering: Improving Visual Place Recognition with Convolutional Calibration","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Adam Jacobson, Michael Milford, Stephen Hausler","submitted_at":"2018-10-30T00:33:51Z","abstract_excerpt":"Convolutional Neural Networks (CNNs) have recently been shown to excel at performing visual place recognition under changing appearance and viewpoint. Previously, place recognition has been improved by intelligently selecting relevant spatial keypoints within a convolutional layer and also by selecting the optimal layer to use. Rather than extracting features out of a particular layer, or a particular set of spatial keypoints within a layer, we propose the extraction of features using a subset of the channel dimensionality within a layer. Each feature map learns to encode a different set of we"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.12465","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":""},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:02:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WtdfPUBc/FVeY6gTqlFAM1cxUsQiG9dC2ZDFbPoQbS3YFSEyX2o4MFSpGuvRhPXakPdvNYzA9XOFFMk2UILNAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-29T01:54:22.206032Z"},"content_sha256":"715290bff419307f0c623232a0b9bfc1858bde007e701ad9d3768b28bfedad51","schema_version":"1.0","event_id":"sha256:715290bff419307f0c623232a0b9bfc1858bde007e701ad9d3768b28bfedad51"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/LKBVNNOAFWOPVOSS2T6QYIXKJY/bundle.json","state_url":"https://pith.science/pith/LKBVNNOAFWOPVOSS2T6QYIXKJY/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/LKBVNNOAFWOPVOSS2T6QYIXKJY/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-29T01:54:22Z","links":{"resolver":"https://pith.science/pith/LKBVNNOAFWOPVOSS2T6QYIXKJY","bundle":"https://pith.science/pith/LKBVNNOAFWOPVOSS2T6QYIXKJY/bundle.json","state":"https://pith.science/pith/LKBVNNOAFWOPVOSS2T6QYIXKJY/state.json","well_known_bundle":"https://pith.science/.well-known/pith/LKBVNNOAFWOPVOSS2T6QYIXKJY/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:LKBVNNOAFWOPVOSS2T6QYIXKJY","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"e97481e148e69773682cfcd8fdd338d908acc484204412d17416ef39c097fea9","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2018-10-30T00:33:51Z","title_canon_sha256":"e60a8772fa689a4e0a8f054959bc189fc7d66eaea2af06130c457ff3843ffcd7"},"schema_version":"1.0","source":{"id":"1810.12465","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1810.12465","created_at":"2026-05-18T00:02:02Z"},{"alias_kind":"arxiv_version","alias_value":"1810.12465v1","created_at":"2026-05-18T00:02:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.12465","created_at":"2026-05-18T00:02:02Z"},{"alias_kind":"pith_short_12","alias_value":"LKBVNNOAFWOP","created_at":"2026-05-18T12:32:37Z"},{"alias_kind":"pith_short_16","alias_value":"LKBVNNOAFWOPVOSS","created_at":"2026-05-18T12:32:37Z"},{"alias_kind":"pith_short_8","alias_value":"LKBVNNOA","created_at":"2026-05-18T12:32:37Z"}],"graph_snapshots":[{"event_id":"sha256:715290bff419307f0c623232a0b9bfc1858bde007e701ad9d3768b28bfedad51","target":"graph","created_at":"2026-05-18T00:02:02Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Convolutional Neural Networks (CNNs) have recently been shown to excel at performing visual place recognition under changing appearance and viewpoint. Previously, place recognition has been improved by intelligently selecting relevant spatial keypoints within a convolutional layer and also by selecting the optimal layer to use. Rather than extracting features out of a particular layer, or a particular set of spatial keypoints within a layer, we propose the extraction of features using a subset of the channel dimensionality within a layer. Each feature map learns to encode a different set of we","authors_text":"Adam Jacobson, Michael Milford, Stephen Hausler","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2018-10-30T00:33:51Z","title":"Feature Map Filtering: Improving Visual Place Recognition with Convolutional Calibration"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.12465","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:5625b6d0a9b9aa33fba01408ddfef638f12e384b032c156ac5627dc0301c6d68","target":"record","created_at":"2026-05-18T00:02:02Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"e97481e148e69773682cfcd8fdd338d908acc484204412d17416ef39c097fea9","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2018-10-30T00:33:51Z","title_canon_sha256":"e60a8772fa689a4e0a8f054959bc189fc7d66eaea2af06130c457ff3843ffcd7"},"schema_version":"1.0","source":{"id":"1810.12465","kind":"arxiv","version":1}},"canonical_sha256":"5a8356b5c02d9cfaba52d4fd0c22ea4e18eca17832b7c8a8cc67f8c3adb536ee","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5a8356b5c02d9cfaba52d4fd0c22ea4e18eca17832b7c8a8cc67f8c3adb536ee","first_computed_at":"2026-05-18T00:02:02.619161Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:02:02.619161Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"TMEZy9qmpRleOCN0QBCZ+P1VINmc+i5rWJxadwzlign3XNe4GOAoq3Cpr/Q2avMl1MR3AVS33so03s6KvS/TAg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:02:02.619653Z","signed_message":"canonical_sha256_bytes"},"source_id":"1810.12465","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5625b6d0a9b9aa33fba01408ddfef638f12e384b032c156ac5627dc0301c6d68","sha256:715290bff419307f0c623232a0b9bfc1858bde007e701ad9d3768b28bfedad51"],"state_sha256":"d47512a6ec3a2e539f312cd863a0dceb99a1b9b37021e91ea12e6ca7ef3adeb2"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tLSPUZAOXXBcK5y825toil18z7igQ6vaM9z8AMPJNUKZwcSPCy5Abu+442IEUI6O8oLp2gv0BT/f36Dh8XhiDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-29T01:54:22.207874Z","bundle_sha256":"58c3f196ab18b8a062e7ba63a66dfc6b6858923cb05194ae21343d9e6811b84e"}}