{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:DHRIJGTYP6Q5QRHNW4U5SFYJXL","short_pith_number":"pith:DHRIJGTY","canonical_record":{"source":{"id":"1810.08151","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2018-10-18T16:42:27Z","cross_cats_sorted":[],"title_canon_sha256":"357fc1fd71c8c3787971923b04f68d8eb8db1251de1eab6239f4fe6d3a894e0f","abstract_canon_sha256":"412ef1a5cdd15ecb920cee6e60394cf656b136b3232df2b939e8627e58125d72"},"schema_version":"1.0"},"canonical_sha256":"19e2849a787fa1d844edb729d91709baee36d2525243419dcac3af2da140230c","source":{"kind":"arxiv","id":"1810.08151","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1810.08151","created_at":"2026-05-17T23:46:36Z"},{"alias_kind":"arxiv_version","alias_value":"1810.08151v2","created_at":"2026-05-17T23:46:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.08151","created_at":"2026-05-17T23:46:36Z"},{"alias_kind":"pith_short_12","alias_value":"DHRIJGTYP6Q5","created_at":"2026-05-18T12:32:19Z"},{"alias_kind":"pith_short_16","alias_value":"DHRIJGTYP6Q5QRHN","created_at":"2026-05-18T12:32:19Z"},{"alias_kind":"pith_short_8","alias_value":"DHRIJGTY","created_at":"2026-05-18T12:32:19Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:DHRIJGTYP6Q5QRHNW4U5SFYJXL","target":"record","payload":{"canonical_record":{"source":{"id":"1810.08151","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2018-10-18T16:42:27Z","cross_cats_sorted":[],"title_canon_sha256":"357fc1fd71c8c3787971923b04f68d8eb8db1251de1eab6239f4fe6d3a894e0f","abstract_canon_sha256":"412ef1a5cdd15ecb920cee6e60394cf656b136b3232df2b939e8627e58125d72"},"schema_version":"1.0"},"canonical_sha256":"19e2849a787fa1d844edb729d91709baee36d2525243419dcac3af2da140230c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:46:36.087531Z","signature_b64":"HOae3+ZPcoC1s4/q/1Otg8cPms5xT7JSwufnOO51mK1XXXhq0q293rWrWoSTNyrMl+WsAN7KIUKJSF8QciNPCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"19e2849a787fa1d844edb729d91709baee36d2525243419dcac3af2da140230c","last_reissued_at":"2026-05-17T23:46:36.087036Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:46:36.087036Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1810.08151","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:46:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3rMUZbxju6qkNCHaxQVbt4sP1sv7ayHwHyjqCgIE8XMvo2WW0Iz8Shy83qUqgx5C6/Hj4FNw+Saw0llM9uitCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-22T18:46:28.829195Z"},"content_sha256":"9427e9f7681bdde35427b1aa69234d7aa2b73c03c6ce0525fa361c8ce9bb9413","schema_version":"1.0","event_id":"sha256:9427e9f7681bdde35427b1aa69234d7aa2b73c03c6ce0525fa361c8ce9bb9413"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:DHRIJGTYP6Q5QRHNW4U5SFYJXL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Probably Unknown: Deep Inverse Sensor Modelling In Radar","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Ingmar Posner, Paul Newman, Rob Weston, Sarah Cen","submitted_at":"2018-10-18T16:42:27Z","abstract_excerpt":"Radar presents a promising alternative to lidar and vision in autonomous vehicle applications, able to detect objects at long range under a variety of weather conditions. However, distinguishing between occupied and free space from raw radar power returns is challenging due to complex interactions between sensor noise and occlusion.\n  To counter this we propose to learn an Inverse Sensor Model (ISM) converting a raw radar scan to a grid map of occupancy probabilities using a deep neural network. Our network is self-supervised using partial occupancy labels generated by lidar, allowing a robot "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.08151","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":""},"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":"2026-05-17T23:46:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rK5Gwar4BEs1oqVh8FkGVs31vmP0z1uVfId5iXk98dGJPv0g7/ytLOwAgYMPh2drhUkmRvxvrV2+9OUzA3sABA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-22T18:46:28.829556Z"},"content_sha256":"ae8919b60c374a848d73530a93c58c5b144d97ede704f412995856131d2e8adf","schema_version":"1.0","event_id":"sha256:ae8919b60c374a848d73530a93c58c5b144d97ede704f412995856131d2e8adf"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/DHRIJGTYP6Q5QRHNW4U5SFYJXL/bundle.json","state_url":"https://pith.science/pith/DHRIJGTYP6Q5QRHNW4U5SFYJXL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/DHRIJGTYP6Q5QRHNW4U5SFYJXL/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":"2026-08-22T18:46:28Z","links":{"resolver":"https://pith.science/pith/DHRIJGTYP6Q5QRHNW4U5SFYJXL","bundle":"https://pith.science/pith/DHRIJGTYP6Q5QRHNW4U5SFYJXL/bundle.json","state":"https://pith.science/pith/DHRIJGTYP6Q5QRHNW4U5SFYJXL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/DHRIJGTYP6Q5QRHNW4U5SFYJXL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:DHRIJGTYP6Q5QRHNW4U5SFYJXL","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":"412ef1a5cdd15ecb920cee6e60394cf656b136b3232df2b939e8627e58125d72","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2018-10-18T16:42:27Z","title_canon_sha256":"357fc1fd71c8c3787971923b04f68d8eb8db1251de1eab6239f4fe6d3a894e0f"},"schema_version":"1.0","source":{"id":"1810.08151","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1810.08151","created_at":"2026-05-17T23:46:36Z"},{"alias_kind":"arxiv_version","alias_value":"1810.08151v2","created_at":"2026-05-17T23:46:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.08151","created_at":"2026-05-17T23:46:36Z"},{"alias_kind":"pith_short_12","alias_value":"DHRIJGTYP6Q5","created_at":"2026-05-18T12:32:19Z"},{"alias_kind":"pith_short_16","alias_value":"DHRIJGTYP6Q5QRHN","created_at":"2026-05-18T12:32:19Z"},{"alias_kind":"pith_short_8","alias_value":"DHRIJGTY","created_at":"2026-05-18T12:32:19Z"}],"graph_snapshots":[{"event_id":"sha256:ae8919b60c374a848d73530a93c58c5b144d97ede704f412995856131d2e8adf","target":"graph","created_at":"2026-05-17T23:46:36Z","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":"Radar presents a promising alternative to lidar and vision in autonomous vehicle applications, able to detect objects at long range under a variety of weather conditions. However, distinguishing between occupied and free space from raw radar power returns is challenging due to complex interactions between sensor noise and occlusion.\n  To counter this we propose to learn an Inverse Sensor Model (ISM) converting a raw radar scan to a grid map of occupancy probabilities using a deep neural network. Our network is self-supervised using partial occupancy labels generated by lidar, allowing a robot ","authors_text":"Ingmar Posner, Paul Newman, Rob Weston, Sarah Cen","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2018-10-18T16:42:27Z","title":"Probably Unknown: Deep Inverse Sensor Modelling In Radar"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.08151","kind":"arxiv","version":2},"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":"sha256:9427e9f7681bdde35427b1aa69234d7aa2b73c03c6ce0525fa361c8ce9bb9413","target":"record","created_at":"2026-05-17T23:46:36Z","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":"412ef1a5cdd15ecb920cee6e60394cf656b136b3232df2b939e8627e58125d72","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2018-10-18T16:42:27Z","title_canon_sha256":"357fc1fd71c8c3787971923b04f68d8eb8db1251de1eab6239f4fe6d3a894e0f"},"schema_version":"1.0","source":{"id":"1810.08151","kind":"arxiv","version":2}},"canonical_sha256":"19e2849a787fa1d844edb729d91709baee36d2525243419dcac3af2da140230c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"19e2849a787fa1d844edb729d91709baee36d2525243419dcac3af2da140230c","first_computed_at":"2026-05-17T23:46:36.087036Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:46:36.087036Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"HOae3+ZPcoC1s4/q/1Otg8cPms5xT7JSwufnOO51mK1XXXhq0q293rWrWoSTNyrMl+WsAN7KIUKJSF8QciNPCg==","signature_status":"signed_v1","signed_at":"2026-05-17T23:46:36.087531Z","signed_message":"canonical_sha256_bytes"},"source_id":"1810.08151","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9427e9f7681bdde35427b1aa69234d7aa2b73c03c6ce0525fa361c8ce9bb9413","sha256:ae8919b60c374a848d73530a93c58c5b144d97ede704f412995856131d2e8adf"],"state_sha256":"2b961e4a3742c831634319fafb8bfcdfce9773f1d2666d09803a089833bd6780"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cCjm0jafb5aYAmH+sX3hsUANZgf+UO9AltdH7UutcxUnODy0iNoePEjOApY3tATIYIWeXDbx6ejVSQryF8RICA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-22T18:46:28.832888Z","bundle_sha256":"3989397d235c292d4124e58a9615899e3a8281585429acaedb62b434a53c7008"}}