{"as_of":"2026-08-11T14:47:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f075465f9ae82b3ca2cfe679dd4eb943cca71e43651796295bbc8621e034f82d","coverage":[{"denominator":53,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":53,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-29T04:38:21.101435Z","state":"measured"},{"denominator":53,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":53,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-11T06:34:44.6726+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2606.27811/citation-record","integrity":"/paper/2606.27811/integrity","json":"/paper/2606.27811/citation-record.json","paper":"/paper/2606.27811"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T04:38:21.101435Z","title":"Present and future of SLAM in extreme environments: The DARPA SubT challenge,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.27811","last_updated":"2026-06-26T07:53:09Z","snapshot_observed_at":"2026-08-02T19:56:26.532905Z","submitted_at":"2026-06-26T07:53:09Z","title":"LXD-SLAM: LiDAR+X Dense SLAM with $\\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-06-29T04:38:21.101435Z"},"links":{"citing_paper":"/paper/2606.27811"},"observation_digest":"sha256:62988793525a101886b57806eb9eee4b7a8eafcbaf7ae852bd4085820e1f7203","observation_id":"30c8853c-c650-45a9-8ba8-d87667d36322","resolution":{"observed_at":"2026-06-29T04:38:21.101435Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T04:38:21.101435Z","title":"Tightly coupled 3D lidar inertial odometry and mapping,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.27811","last_updated":"2026-06-26T07:53:09Z","snapshot_observed_at":"2026-08-02T19:56:26.532905Z","submitted_at":"2026-06-26T07:53:09Z","title":"LXD-SLAM: LiDAR+X Dense SLAM with $\\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-06-29T04:38:21.101435Z"},"links":{"citing_paper":"/paper/2606.27811"},"observation_digest":"sha256:d9fd055b3486530336877ed79b4466ca919e6369afe9f9d1bd2d0f8bfd673b65","observation_id":"00e923c3-dfaa-43a2-91c0-d96c869abf46","resolution":{"observed_at":"2026-06-29T04:38:21.101435Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T04:38:21.101435Z","title":"LIO-SAM: Tightly-coupled lidar inertial odometry via smoothing and mapping,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.27811","last_updated":"2026-06-26T07:53:09Z","snapshot_observed_at":"2026-08-02T19:56:26.532905Z","submitted_at":"2026-06-26T07:53:09Z","title":"LXD-SLAM: LiDAR+X Dense SLAM with $\\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-06-29T04:38:21.101435Z"},"links":{"citing_paper":"/paper/2606.27811"},"observation_digest":"sha256:a7340fbd179209d77fe23ba02e0b574a613aefe01bb01cfc81fa4d277c9cf4bc","observation_id":"9adccddd-9b60-4f2d-ae13-249d237d70f5","resolution":{"observed_at":"2026-06-29T04:38:21.101435Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T04:38:21.101435Z","title":"LIO-EKF: High frequency lidar-inertial odometry using extended kalman filters,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.27811","last_updated":"2026-06-26T07:53:09Z","snapshot_observed_at":"2026-08-02T19:56:26.532905Z","submitted_at":"2026-06-26T07:53:09Z","title":"LXD-SLAM: LiDAR+X Dense SLAM with $\\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-06-29T04:38:21.101435Z"},"links":{"citing_paper":"/paper/2606.27811"},"observation_digest":"sha256:6f8c3e38ed08e2cac385abe374a2b4c9c34b83f59215f833d2f9b1d91dcad366","observation_id":"e1a2b8a9-39d2-401f-995c-bcdbee39cc5f","resolution":{"observed_at":"2026-06-29T04:38:21.101435Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T04:38:21.101435Z","title":"LINS: A lidar-inertial state estimator for robust and efficient navigation,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.27811","last_updated":"2026-06-26T07:53:09Z","snapshot_observed_at":"2026-08-02T19:56:26.532905Z","submitted_at":"2026-06-26T07:53:09Z","title":"LXD-SLAM: LiDAR+X Dense SLAM with $\\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-06-29T04:38:21.101435Z"},"links":{"citing_paper":"/paper/2606.27811"},"observation_digest":"sha256:d13d05cc1a00cc8dc7d898d6d61d386cb4b7a22f2f2236c5f43e178b63d47041","observation_id":"c1291aee-41fb-4405-aa74-c9cf6e324349","resolution":{"observed_at":"2026-06-29T04:38:21.101435Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T04:38:21.101435Z","title":"FAST-LIO2: Fast direct lidar-inertial odometry,","venue":null,"work_id":null,"year":2053},"citing_paper":{"arxiv_id":"2606.27811","last_updated":"2026-06-26T07:53:09Z","snapshot_observed_at":"2026-08-02T19:56:26.532905Z","submitted_at":"2026-06-26T07:53:09Z","title":"LXD-SLAM: LiDAR+X Dense SLAM with $\\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-06-29T04:38:21.101435Z"},"links":{"citing_paper":"/paper/2606.27811"},"observation_digest":"sha256:fbbe8c123f7c223797c6b25198d94cae20bf619cb6d5f0fea8ecf67990077e9c","observation_id":"8bf44bc7-13f5-4ea7-9ac0-2b6d35fc044c","resolution":{"observed_at":"2026-06-29T04:38:21.101435Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T04:38:21.101435Z","title":"Faster-LIO: Lightweight tightly coupled lidar-inertial odometry using parallel sparse incremental voxels,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.27811","last_updated":"2026-06-26T07:53:09Z","snapshot_observed_at":"2026-08-02T19:56:26.532905Z","submitted_at":"2026-06-26T07:53:09Z","title":"LXD-SLAM: LiDAR+X Dense SLAM with $\\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-06-29T04:38:21.101435Z"},"links":{"citing_paper":"/paper/2606.27811"},"observation_digest":"sha256:d4ec9a3fe88c23dca065dac159e365412fa11ff40f710fac20658783ddef1696","observation_id":"f72b5d1c-3ff0-4d5c-9c14-7f86916764de","resolution":{"observed_at":"2026-06-29T04:38:21.101435Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T04:38:21.101435Z","title":"Visual-lidar odometry and mapping: low-drift, robust, and fast,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2606.27811","last_updated":"2026-06-26T07:53:09Z","snapshot_observed_at":"2026-08-02T19:56:26.532905Z","submitted_at":"2026-06-26T07:53:09Z","title":"LXD-SLAM: LiDAR+X Dense SLAM with $\\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-06-29T04:38:21.101435Z"},"links":{"citing_paper":"/paper/2606.27811"},"observation_digest":"sha256:6832833b10f227e1196c8ac9df9dc473eb3f862995abcd820ae7ecce4da26a65","observation_id":"16eb5ed8-3984-46e7-880c-a9b014767989","resolution":{"observed_at":"2026-06-29T04:38:21.101435Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T04:38:21.101435Z","title":"LVI-SAM: Tightly-coupled lidar-visual-inertial odometry via smoothing and mapping,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.27811","last_updated":"2026-06-26T07:53:09Z","snapshot_observed_at":"2026-08-02T19:56:26.532905Z","submitted_at":"2026-06-26T07:53:09Z","title":"LXD-SLAM: LiDAR+X Dense SLAM with $\\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-06-29T04:38:21.101435Z"},"links":{"citing_paper":"/paper/2606.27811"},"observation_digest":"sha256:900cee1ac44326fb7cfe4b08ad8c9b18617bde49ad971a498ea67f0bb2918652","observation_id":"a73914a9-27f2-4a8d-94ba-bbd1bd207fca","resolution":{"observed_at":"2026-06-29T04:38:21.101435Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T04:38:21.101435Z","title":"LIC-Fusion: Lidar- inertial-camera odometry,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.27811","last_updated":"2026-06-26T07:53:09Z","snapshot_observed_at":"2026-08-02T19:56:26.532905Z","submitted_at":"2026-06-26T07:53:09Z","title":"LXD-SLAM: LiDAR+X Dense SLAM with $\\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-06-29T04:38:21.101435Z"},"links":{"citing_paper":"/paper/2606.27811"},"observation_digest":"sha256:be72141e382d73537818aeefef591222101c3ae16d93c5ad592fceb423b2c4fe","observation_id":"a458201d-4636-4d10-800e-fc75db5cb67c","resolution":{"observed_at":"2026-06-29T04:38:21.101435Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T04:38:21.101435Z","title":"R 2LIVE: A robust, real-time, lidar-inertial-visual tightly-coupled state estimator and mapping,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.27811","last_updated":"2026-06-26T07:53:09Z","snapshot_observed_at":"2026-08-02T19:56:26.532905Z","submitted_at":"2026-06-26T07:53:09Z","title":"LXD-SLAM: LiDAR+X Dense SLAM with $\\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-06-29T04:38:21.101435Z"},"links":{"citing_paper":"/paper/2606.27811"},"observation_digest":"sha256:cff7dc5f1a5eedc0f22fd27b24a3899791328cac9a133dba7794736f565b5ca9","observation_id":"369f8f87-5e1e-48a3-a4ab-c41e86a36973","resolution":{"observed_at":"2026-06-29T04:38:21.101435Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T04:38:21.101435Z","title":"R3LIVE: A robust, real-time, RGB-colored, LiDAR- inertial-visual tightly-coupled state estimation and mapping package,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.27811","last_updated":"2026-06-26T07:53:09Z","snapshot_observed_at":"2026-08-02T19:56:26.532905Z","submitted_at":"2026-06-26T07:53:09Z","title":"LXD-SLAM: LiDAR+X Dense SLAM with $\\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-06-29T04:38:21.101435Z"},"links":{"citing_paper":"/paper/2606.27811"},"observation_digest":"sha256:778be86df8ea03429186716ba9fc560680b016e6b1ed24d327b445495cd0e3f0","observation_id":"dbfdcc81-d875-4b51-8977-65930d069a65","resolution":{"observed_at":"2026-06-29T04:38:21.101435Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T04:38:21.101435Z","title":"FAST-LIVO: Fast and tightly-coupled sparse-direct LiDAR-inertial-visual odometry,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.27811","last_updated":"2026-06-26T07:53:09Z","snapshot_observed_at":"2026-08-02T19:56:26.532905Z","submitted_at":"2026-06-26T07:53:09Z","title":"LXD-SLAM: LiDAR+X Dense SLAM with $\\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-06-29T04:38:21.101435Z"},"links":{"citing_paper":"/paper/2606.27811"},"observation_digest":"sha256:174d44acd61bcafd997c79804453d03727cf788d015450babb93873e5ef70464","observation_id":"984f6f4a-b240-44eb-955a-dcd31151ee1b","resolution":{"observed_at":"2026-06-29T04:38:21.101435Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T04:38:21.101435Z","title":"FAST-LIVO2: Fast, direct lidar–inertial–visual odometry,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.27811","last_updated":"2026-06-26T07:53:09Z","snapshot_observed_at":"2026-08-02T19:56:26.532905Z","submitted_at":"2026-06-26T07:53:09Z","title":"LXD-SLAM: LiDAR+X Dense SLAM with $\\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-06-29T04:38:21.101435Z"},"links":{"citing_paper":"/paper/2606.27811"},"observation_digest":"sha256:b0911bda84e1620973aeb359307c47093aaa0bb8a4383f0dd0e884bf4afbc52f","observation_id":"a11503c9-c42e-46e6-aff6-0d37a3dce7b8","resolution":{"observed_at":"2026-06-29T04:38:21.101435Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T04:38:21.101435Z","title":"Robust odometry and mapping for multi-lidar systems with online extrinsic calibration,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.27811","last_updated":"2026-06-26T07:53:09Z","snapshot_observed_at":"2026-08-02T19:56:26.532905Z","submitted_at":"2026-06-26T07:53:09Z","title":"LXD-SLAM: LiDAR+X Dense SLAM with $\\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-06-29T04:38:21.101435Z"},"links":{"citing_paper":"/paper/2606.27811"},"observation_digest":"sha256:5e26bf0ff6ad125aab03ea289e3fdab9f9478a3292e7ff8829b889e6df7a64a4","observation_id":"e9359148-27d1-4d2e-903d-6498e479b601","resolution":{"observed_at":"2026-06-29T04:38:21.101435Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T04:38:21.101435Z","title":"LIWO: LiDAR-inertial-wheel odometry,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.27811","last_updated":"2026-06-26T07:53:09Z","snapshot_observed_at":"2026-08-02T19:56:26.532905Z","submitted_at":"2026-06-26T07:53:09Z","title":"LXD-SLAM: LiDAR+X Dense SLAM with $\\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-06-29T04:38:21.101435Z"},"links":{"citing_paper":"/paper/2606.27811"},"observation_digest":"sha256:85b27ec2c6bcbbda65fc942b93a77361621e2e21471fe1c45409d6bfd9321dac","observation_id":"d324f97e-647c-4c3b-bc28-62bd2019f5b5","resolution":{"observed_at":"2026-06-29T04:38:21.101435Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1901.03642","last_updated":"2019-01-11T16:41:12Z","snapshot_observed_at":"2026-07-31T16:10:30.153516Z","submitted_at":"2019-01-11T16:41:12Z","title":"A General Optimization-based Framework for Global Pose Estimation with Multiple Sensors","version":1},"cited_work":{"arxiv_id":"1901.03642","doi":null,"metadata_source":"pith","pith_arxiv_id":"1901.03642","snapshot_observed_at":"2026-07-04T19:30:07.895807Z","title":"A General Optimization-based Framework for Global Pose Estimation with Multiple Sensors","venue":"cs.CV","work_id":"78a9ff5d-d04f-4ec6-9eba-be715159e9e2","year":2019},"citing_paper":{"arxiv_id":"2606.27811","last_updated":"2026-06-26T07:53:09Z","snapshot_observed_at":"2026-08-02T19:56:26.532905Z","submitted_at":"2026-06-26T07:53:09Z","title":"LXD-SLAM: LiDAR+X Dense SLAM with $\\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-06-29T04:38:21.101435Z"},"links":{"cited_paper":"/paper/1901.03642","citing_paper":"/paper/2606.27811"},"observation_digest":"sha256:e15780c6bd219857a7c18631b8698549f07863e41fc882c9019ec2a197ecc543","observation_id":"0fec9c36-0ccb-423e-ab2b-08180b3dd8c2","resolution":{"observed_at":"2026-06-29T19:43:55.304310Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T04:38:21.101435Z","title":"GVINS: Tightly coupled gnss–visual–inertial fusion for smooth and consistent state estimation,","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2606.27811","last_updated":"2026-06-26T07:53:09Z","snapshot_observed_at":"2026-08-02T19:56:26.532905Z","submitted_at":"2026-06-26T07:53:09Z","title":"LXD-SLAM: LiDAR+X Dense SLAM with $\\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-06-29T04:38:21.101435Z"},"links":{"citing_paper":"/paper/2606.27811"},"observation_digest":"sha256:bc88f11446215468b4b9dc23f99fcb2168c2f78e9fc003eb16290dac52c59a4a","observation_id":"f785dc76-0ec0-49d2-a7f9-9ff8a88e175c","resolution":{"observed_at":"2026-06-29T04:38:21.101435Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T04:38:21.101435Z","title":"LOAM: Lidar odometry and mapping in real- time,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2606.27811","last_updated":"2026-06-26T07:53:09Z","snapshot_observed_at":"2026-08-02T19:56:26.532905Z","submitted_at":"2026-06-26T07:53:09Z","title":"LXD-SLAM: LiDAR+X Dense SLAM with $\\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-06-29T04:38:21.101435Z"},"links":{"citing_paper":"/paper/2606.27811"},"observation_digest":"sha256:8ccbfdce4bee8578255441fa215cff3b5ef1c9a182c7d710c2f3a67aa3fe38e0","observation_id":"34d15146-76bd-486a-8c48-6cf0b03c25e7","resolution":{"observed_at":"2026-06-29T04:38:21.101435Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T04:38:21.101435Z","title":"LeGO-LOAM: Lightweight and ground-optimized lidar odometry and mapping on variable terrain,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.27811","last_updated":"2026-06-26T07:53:09Z","snapshot_observed_at":"2026-08-02T19:56:26.532905Z","submitted_at":"2026-06-26T07:53:09Z","title":"LXD-SLAM: LiDAR+X Dense SLAM with $\\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-06-29T04:38:21.101435Z"},"links":{"citing_paper":"/paper/2606.27811"},"observation_digest":"sha256:0dd1a2d1031a77e4c83f7790abf00451834e406cfc9661729ffe59251f26a096","observation_id":"d4972b84-870e-45a0-af2d-8be04dbaffa4","resolution":{"observed_at":"2026-06-29T04:38:21.101435Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T04:38:21.101435Z","title":"F-LOAM : Fast lidar odometry and mapping,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.27811","last_updated":"2026-06-26T07:53:09Z","snapshot_observed_at":"2026-08-02T19:56:26.532905Z","submitted_at":"2026-06-26T07:53:09Z","title":"LXD-SLAM: LiDAR+X Dense SLAM with $\\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-06-29T04:38:21.101435Z"},"links":{"citing_paper":"/paper/2606.27811"},"observation_digest":"sha256:7e7ed0d16ce15c65bde5a8a232d8c483d6c21c0e719bc23267c14a26b0cd0377","observation_id":"7ed45d2a-8d52-4e24-b8b5-e52b482972c2","resolution":{"observed_at":"2026-06-29T04:38:21.101435Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T04:38:21.101435Z","title":"FAST-LIO: A fast, robust lidar-inertial odometry package by tightly-coupled iterated kalman filter,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.27811","last_updated":"2026-06-26T07:53:09Z","snapshot_observed_at":"2026-08-02T19:56:26.532905Z","submitted_at":"2026-06-26T07:53:09Z","title":"LXD-SLAM: LiDAR+X Dense SLAM with $\\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-06-29T04:38:21.101435Z"},"links":{"citing_paper":"/paper/2606.27811"},"observation_digest":"sha256:1b57efe30fab8bd1b107477d6224720dc6e85ba56eb71f65490cd8615d0d41d9","observation_id":"6ab14b0c-ec07-43a9-8cd3-4f446cf660b1","resolution":{"observed_at":"2026-06-29T04:38:21.101435Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T04:38:21.101435Z","title":"Point-LIO: Robust high-bandwidth light detection and ranging inertial odometry,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.27811","last_updated":"2026-06-26T07:53:09Z","snapshot_observed_at":"2026-08-02T19:56:26.532905Z","submitted_at":"2026-06-26T07:53:09Z","title":"LXD-SLAM: LiDAR+X Dense SLAM with $\\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-06-29T04:38:21.101435Z"},"links":{"citing_paper":"/paper/2606.27811"},"observation_digest":"sha256:71145a90148d37b9728bccff44eea0fae6de621cfd6b0e908fd8850c8903ea83","observation_id":"6b69d973-210b-4f74-b25e-71dd8cec8e65","resolution":{"observed_at":"2026-06-29T04:38:21.101435Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T04:38:21.101435Z","title":"DEMO: Depth enhanced monocular odometry,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2606.27811","last_updated":"2026-06-26T07:53:09Z","snapshot_observed_at":"2026-08-02T19:56:26.532905Z","submitted_at":"2026-06-26T07:53:09Z","title":"LXD-SLAM: LiDAR+X Dense SLAM with $\\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-06-29T04:38:21.101435Z"},"links":{"citing_paper":"/paper/2606.27811"},"observation_digest":"sha256:6f3b74068250c2b22369793b8df4f6f8ebf932144785b5c125a0b196fc411b57","observation_id":"249f85ad-0afc-4ece-8480-66976c6263a9","resolution":{"observed_at":"2026-06-29T04:38:21.101435Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T04:38:21.101435Z","title":"LIC-Fusion 2.0: Lidar-inertial-camera odometry with sliding-window plane-feature tracking,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.27811","last_updated":"2026-06-26T07:53:09Z","snapshot_observed_at":"2026-08-02T19:56:26.532905Z","submitted_at":"2026-06-26T07:53:09Z","title":"LXD-SLAM: LiDAR+X Dense SLAM with $\\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-06-29T04:38:21.101435Z"},"links":{"citing_paper":"/paper/2606.27811"},"observation_digest":"sha256:bcaa29ba55ff2aa01596868c5697ef193b798a49bd82008119fdda83d3e036e7","observation_id":"b1f2e64f-fd21-4c0a-8874-2d496c9a3d9e","resolution":{"observed_at":"2026-06-29T04:38:21.101435Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T04:38:21.101435Z","title":"VINS-Mono: A robust and versatile monocular visual-inertial state estimator,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.27811","last_updated":"2026-06-26T07:53:09Z","snapshot_observed_at":"2026-08-02T19:56:26.532905Z","submitted_at":"2026-06-26T07:53:09Z","title":"LXD-SLAM: LiDAR+X Dense SLAM with $\\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-06-29T04:38:21.101435Z"},"links":{"citing_paper":"/paper/2606.27811"},"observation_digest":"sha256:6cc7b3d8ee7ab951cc33d4e7d6163a6cfa564bd141360767ac1c076e79d0c853","observation_id":"caf60558-951b-4a98-a099-b5168c33659a","resolution":{"observed_at":"2026-06-29T04:38:21.101435Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T04:38:21.101435Z","title":"Scalability in perception for autonomous driving: Waymo open dataset,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.27811","last_updated":"2026-06-26T07:53:09Z","snapshot_observed_at":"2026-08-02T19:56:26.532905Z","submitted_at":"2026-06-26T07:53:09Z","title":"LXD-SLAM: LiDAR+X Dense SLAM with $\\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-06-29T04:38:21.101435Z"},"links":{"citing_paper":"/paper/2606.27811"},"observation_digest":"sha256:b353f0156f58437d8cc27e033293083e9fbcb07995aa5c2b3926f647ff050f76","observation_id":"23fb89e1-fe29-4e0f-b516-dbb5aed9702f","resolution":{"observed_at":"2026-06-29T04:38:21.101435Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T04:38:21.101435Z","title":"nuscenes: A multimodal dataset for autonomous driving,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.27811","last_updated":"2026-06-26T07:53:09Z","snapshot_observed_at":"2026-08-02T19:56:26.532905Z","submitted_at":"2026-06-26T07:53:09Z","title":"LXD-SLAM: LiDAR+X Dense SLAM with $\\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-06-29T04:38:21.101435Z"},"links":{"citing_paper":"/paper/2606.27811"},"observation_digest":"sha256:0bbffb9a7aca872900b5a7a95fe3daccce3a10987839e7855161ab0577c18853","observation_id":"86242e6d-6282-440f-b5a8-7b96a7e385b4","resolution":{"observed_at":"2026-06-29T04:38:21.101435Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T04:38:21.101435Z","title":"KITTI-360: A novel dataset and bench- marks for urban scene understanding in 2D and 3D,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.27811","last_updated":"2026-06-26T07:53:09Z","snapshot_observed_at":"2026-08-02T19:56:26.532905Z","submitted_at":"2026-06-26T07:53:09Z","title":"LXD-SLAM: LiDAR+X Dense SLAM with $\\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-06-29T04:38:21.101435Z"},"links":{"citing_paper":"/paper/2606.27811"},"observation_digest":"sha256:a1ee978e963435746824cc7f60c3c75681aaf41f4ce71cb243e5d4bb5ee4c22a","observation_id":"4b9024c6-4965-4036-b0fc-7a71d1452563","resolution":{"observed_at":"2026-06-29T04:38:21.101435Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T04:38:21.101435Z","title":"MLS-SLAM: Multi-level submap guided lidar SLAM,","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2606.27811","last_updated":"2026-06-26T07:53:09Z","snapshot_observed_at":"2026-08-02T19:56:26.532905Z","submitted_at":"2026-06-26T07:53:09Z","title":"LXD-SLAM: LiDAR+X Dense SLAM with $\\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-06-29T04:38:21.101435Z"},"links":{"citing_paper":"/paper/2606.27811"},"observation_digest":"sha256:2f4769790744e2b781cec0ebe86813a2d37f5948b2b63715091d4030796cefe9","observation_id":"f25fcf19-50ff-474e-a2e0-69e38ce1c6d2","resolution":{"observed_at":"2026-06-29T04:38:21.101435Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T04:38:21.101435Z","title":"MLIOM-AB: Multi-lidar-inertial-odometry and mapping for autonomous buses,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.27811","last_updated":"2026-06-26T07:53:09Z","snapshot_observed_at":"2026-08-02T19:56:26.532905Z","submitted_at":"2026-06-26T07:53:09Z","title":"LXD-SLAM: LiDAR+X Dense SLAM with $\\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-06-29T04:38:21.101435Z"},"links":{"citing_paper":"/paper/2606.27811"},"observation_digest":"sha256:3353b58970db40c305eb231fbd923f583aeb4b6d403e0d6b7b9c72d970eb913f","observation_id":"517e6bc2-cebe-4d04-9540-600441ab77de","resolution":{"observed_at":"2026-06-29T04:38:21.101435Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T04:38:21.101435Z","title":"Multi-LIO: A lightweight multiple lidar-inertial odometry system,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.27811","last_updated":"2026-06-26T07:53:09Z","snapshot_observed_at":"2026-08-02T19:56:26.532905Z","submitted_at":"2026-06-26T07:53:09Z","title":"LXD-SLAM: LiDAR+X Dense SLAM with $\\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-06-29T04:38:21.101435Z"},"links":{"citing_paper":"/paper/2606.27811"},"observation_digest":"sha256:86aa80a9adf0ae4413dd958186ad6daafdd07daee2c77e749dfb1274c0dcc378","observation_id":"845eccbc-65f1-4ce9-880f-905d02731e2a","resolution":{"observed_at":"2026-06-29T04:38:21.101435Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T04:38:21.101435Z","title":"VINS on wheels,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2606.27811","last_updated":"2026-06-26T07:53:09Z","snapshot_observed_at":"2026-08-02T19:56:26.532905Z","submitted_at":"2026-06-26T07:53:09Z","title":"LXD-SLAM: LiDAR+X Dense SLAM with $\\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-06-29T04:38:21.101435Z"},"links":{"citing_paper":"/paper/2606.27811"},"observation_digest":"sha256:473cfed06149b7ef36ce2d69e8c8510abde346dda6b94085de8cbac04a5ee809","observation_id":"407fb234-53c1-4aae-a2f5-04d365234654","resolution":{"observed_at":"2026-06-29T04:38:21.101435Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T04:38:21.101435Z","title":"Efficient surfel-based SLAM using 3D laser range data in urban environments,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.27811","last_updated":"2026-06-26T07:53:09Z","snapshot_observed_at":"2026-08-02T19:56:26.532905Z","submitted_at":"2026-06-26T07:53:09Z","title":"LXD-SLAM: LiDAR+X Dense SLAM with $\\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-06-29T04:38:21.101435Z"},"links":{"citing_paper":"/paper/2606.27811"},"observation_digest":"sha256:9138288ca82604ebb9f9c40e922568f1ea39aab7095ae78cfbb83ba148381f2d","observation_id":"83f098c0-54e6-4ef8-b68f-2da2581a0390","resolution":{"observed_at":"2026-06-29T04:38:21.101435Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T04:38:21.101435Z","title":"SuMa++: Efficient lidar-based semantic SLAM,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.27811","last_updated":"2026-06-26T07:53:09Z","snapshot_observed_at":"2026-08-02T19:56:26.532905Z","submitted_at":"2026-06-26T07:53:09Z","title":"LXD-SLAM: LiDAR+X Dense SLAM with $\\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-06-29T04:38:21.101435Z"},"links":{"citing_paper":"/paper/2606.27811"},"observation_digest":"sha256:7e382852b767e65348e172b9c6451680d9cfa9103e2392118e71ec6e8a6bea0c","observation_id":"b4eacb04-b758-4351-9db3-950f290deb03","resolution":{"observed_at":"2026-06-29T04:38:21.101435Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T04:38:21.101435Z","title":"Efficient and probabilistic adaptive voxel mapping for accurate online lidar odometry,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.27811","last_updated":"2026-06-26T07:53:09Z","snapshot_observed_at":"2026-08-02T19:56:26.532905Z","submitted_at":"2026-06-26T07:53:09Z","title":"LXD-SLAM: LiDAR+X Dense SLAM with $\\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-06-29T04:38:21.101435Z"},"links":{"citing_paper":"/paper/2606.27811"},"observation_digest":"sha256:0745620ee3f374d0a9885ad0a20419099830c38de5164a2f2a5f17cb54e66a21","observation_id":"be3b35f0-8753-42e8-ab65-eaa298a133d9","resolution":{"observed_at":"2026-06-29T04:38:21.101435Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T04:38:21.101435Z","title":"BALM: Bundle adjustment for lidar mapping,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.27811","last_updated":"2026-06-26T07:53:09Z","snapshot_observed_at":"2026-08-02T19:56:26.532905Z","submitted_at":"2026-06-26T07:53:09Z","title":"LXD-SLAM: LiDAR+X Dense SLAM with $\\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-06-29T04:38:21.101435Z"},"links":{"citing_paper":"/paper/2606.27811"},"observation_digest":"sha256:2360fb198d122e75c919e0c0e4eee9518aac213ada1fdd0326e63b7962cc0baf","observation_id":"fa7488b8-f5a2-4e27-8302-246840e735da","resolution":{"observed_at":"2026-06-29T04:38:21.101435Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T04:38:21.101435Z","title":"NICE-SLAM: Neural implicit scalable encoding for SLAM,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.27811","last_updated":"2026-06-26T07:53:09Z","snapshot_observed_at":"2026-08-02T19:56:26.532905Z","submitted_at":"2026-06-26T07:53:09Z","title":"LXD-SLAM: LiDAR+X Dense SLAM with $\\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-06-29T04:38:21.101435Z"},"links":{"citing_paper":"/paper/2606.27811"},"observation_digest":"sha256:49b43ff455ddd8e7f960c981b0ee974a3cdf1437323f08d927b89c6123febe1c","observation_id":"1efb3bd3-dfaa-4fa9-8735-5dcb72bb0179","resolution":{"observed_at":"2026-06-29T04:38:21.101435Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T04:38:21.101435Z","title":"Gaussian splatting SLAM,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.27811","last_updated":"2026-06-26T07:53:09Z","snapshot_observed_at":"2026-08-02T19:56:26.532905Z","submitted_at":"2026-06-26T07:53:09Z","title":"LXD-SLAM: LiDAR+X Dense SLAM with $\\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-06-29T04:38:21.101435Z"},"links":{"citing_paper":"/paper/2606.27811"},"observation_digest":"sha256:78e4ad083bea7396222cfc910e7114bb2d7a23e1911576cb9bc4881da80d6aa4","observation_id":"0bea8848-b743-4a26-bffb-99ee799ac2cf","resolution":{"observed_at":"2026-06-29T04:38:21.101435Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T04:38:21.101435Z","title":"GP-SLAM+: real-time 3D lidar SLAM based on improved regionalized Gaussian process map reconstruction,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.27811","last_updated":"2026-06-26T07:53:09Z","snapshot_observed_at":"2026-08-02T19:56:26.532905Z","submitted_at":"2026-06-26T07:53:09Z","title":"LXD-SLAM: LiDAR+X Dense SLAM with $\\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-06-29T04:38:21.101435Z"},"links":{"citing_paper":"/paper/2606.27811"},"observation_digest":"sha256:652b25ffc1e61e140f1379e4d62d8ddc2e0fc918d10a75bc14a7b6773ec6dedd","observation_id":"af578fda-c134-476c-b7c2-96cd5ab97259","resolution":{"observed_at":"2026-06-29T04:38:21.101435Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T04:38:21.101435Z","title":"SLAMesh: Real-time lidar simul- taneous localization and meshing,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.27811","last_updated":"2026-06-26T07:53:09Z","snapshot_observed_at":"2026-08-02T19:56:26.532905Z","submitted_at":"2026-06-26T07:53:09Z","title":"LXD-SLAM: LiDAR+X Dense SLAM with $\\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-06-29T04:38:21.101435Z"},"links":{"citing_paper":"/paper/2606.27811"},"observation_digest":"sha256:0224fd61b473596665fb73a1fc49b52575f752ca73041602e81af7f934e5c746","observation_id":"e5258a3d-5b5e-435a-ae89-4eab1a2dd5db","resolution":{"observed_at":"2026-06-29T04:38:21.101435Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T04:38:21.101435Z","title":"Symbolic representation and toolkit development of iterated error-state extended kalman filters on manifolds,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.27811","last_updated":"2026-06-26T07:53:09Z","snapshot_observed_at":"2026-08-02T19:56:26.532905Z","submitted_at":"2026-06-26T07:53:09Z","title":"LXD-SLAM: LiDAR+X Dense SLAM with $\\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-06-29T04:38:21.101435Z"},"links":{"citing_paper":"/paper/2606.27811"},"observation_digest":"sha256:0a44eace1a9a170f810a436948f8c1be898f01cd95dcc73b3ee3a977d0d51cb5","observation_id":"d914b232-5a91-4a6b-822a-8e4ed4b594b7","resolution":{"observed_at":"2026-06-29T04:38:21.101435Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T04:38:21.101435Z","title":"Fast ray-tracing of rectilinear volume data using distance transforms,","venue":null,"work_id":null,"year":2000},"citing_paper":{"arxiv_id":"2606.27811","last_updated":"2026-06-26T07:53:09Z","snapshot_observed_at":"2026-08-02T19:56:26.532905Z","submitted_at":"2026-06-26T07:53:09Z","title":"LXD-SLAM: LiDAR+X Dense SLAM with $\\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-06-29T04:38:21.101435Z"},"links":{"citing_paper":"/paper/2606.27811"},"observation_digest":"sha256:2bdd3df9ac398aa325c1d6091c059ec96aa0d019efb60944fb64338a28e00fed","observation_id":"7864a024-1ab1-47fa-8309-11316333b642","resolution":{"observed_at":"2026-06-29T04:38:21.101435Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T04:38:21.101435Z","title":"Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography,","venue":null,"work_id":null,"year":1981},"citing_paper":{"arxiv_id":"2606.27811","last_updated":"2026-06-26T07:53:09Z","snapshot_observed_at":"2026-08-02T19:56:26.532905Z","submitted_at":"2026-06-26T07:53:09Z","title":"LXD-SLAM: LiDAR+X Dense SLAM with $\\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-06-29T04:38:21.101435Z"},"links":{"citing_paper":"/paper/2606.27811"},"observation_digest":"sha256:c86772710887e27b5c325d06de87ee86846981d6dd6a67ee420bc04a7b5af8dd","observation_id":"79f2819e-5712-4298-b920-78da90859587","resolution":{"observed_at":"2026-06-29T04:38:21.101435Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T04:38:21.101435Z","title":"NTU VIRAL: A Visual-Inertial-Ranging-Lidar Dataset, from an Aerial Vehicle Viewpoint,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.27811","last_updated":"2026-06-26T07:53:09Z","snapshot_observed_at":"2026-08-02T19:56:26.532905Z","submitted_at":"2026-06-26T07:53:09Z","title":"LXD-SLAM: LiDAR+X Dense SLAM with $\\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-06-29T04:38:21.101435Z"},"links":{"citing_paper":"/paper/2606.27811"},"observation_digest":"sha256:3bbd1c974c03ea4db7eb49a086620fcf89d706efa7e527568478b663591129ab","observation_id":"8473e7b9-c4cf-40e0-b1e6-7113564b814f","resolution":{"observed_at":"2026-06-29T04:38:21.101435Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T04:38:21.101435Z","title":"FusionPortableV2: A Unified Multi-Sensor Dataset for Generalized SLAM Across Diverse Platforms and Scalable Environments,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.27811","last_updated":"2026-06-26T07:53:09Z","snapshot_observed_at":"2026-08-02T19:56:26.532905Z","submitted_at":"2026-06-26T07:53:09Z","title":"LXD-SLAM: LiDAR+X Dense SLAM with $\\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-06-29T04:38:21.101435Z"},"links":{"citing_paper":"/paper/2606.27811"},"observation_digest":"sha256:773ce29f5a52a0c8dccb789aa34e2100438bbc68dbdb9a6cb42977bdd6cc5e50","observation_id":"1c8fa3e1-9720-4ec2-b774-f184aa6de771","resolution":{"observed_at":"2026-06-29T04:38:21.101435Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T04:38:21.101435Z","title":"Are we ready for autonomous driving? the KITTI vision benchmark suite,","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2606.27811","last_updated":"2026-06-26T07:53:09Z","snapshot_observed_at":"2026-08-02T19:56:26.532905Z","submitted_at":"2026-06-26T07:53:09Z","title":"LXD-SLAM: LiDAR+X Dense SLAM with $\\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-06-29T04:38:21.101435Z"},"links":{"citing_paper":"/paper/2606.27811"},"observation_digest":"sha256:5447680bb1f58c2c77e92454737c957c21203c3c4870ab9ae397cb5cbe8d2d60","observation_id":"82c7f57c-649c-4dfd-935c-7e0d9ec7bb5d","resolution":{"observed_at":"2026-06-29T04:38:21.101435Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T04:38:21.101435Z","title":"SDV-LOAM: Semi- direct visual–lidar odometry and mapping,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.27811","last_updated":"2026-06-26T07:53:09Z","snapshot_observed_at":"2026-08-02T19:56:26.532905Z","submitted_at":"2026-06-26T07:53:09Z","title":"LXD-SLAM: LiDAR+X Dense SLAM with $\\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-06-29T04:38:21.101435Z"},"links":{"citing_paper":"/paper/2606.27811"},"observation_digest":"sha256:907a4d2e3310dad2762c868c09b47eb134043304550d9bfd63c63538c65c2b42","observation_id":"d68ff18b-0ca3-4b08-8d8f-861e6940654b","resolution":{"observed_at":"2026-06-29T04:38:21.101435Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T04:38:21.101435Z","title":"D-LIOM: Tightly-coupled direct lidar-inertial odometry and mapping,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.27811","last_updated":"2026-06-26T07:53:09Z","snapshot_observed_at":"2026-08-02T19:56:26.532905Z","submitted_at":"2026-06-26T07:53:09Z","title":"LXD-SLAM: LiDAR+X Dense SLAM with $\\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-06-29T04:38:21.101435Z"},"links":{"citing_paper":"/paper/2606.27811"},"observation_digest":"sha256:be68a1ba9e8ebc7211770b5798a81e90e27e16db036641199c5bc3d3ec89aa0b","observation_id":"35aa0b03-51c2-4cc5-9963-2e5d7cb3ebdf","resolution":{"observed_at":"2026-06-29T04:38:21.101435Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T04:38:21.101435Z","title":"Ct-LVI: A framework toward continuous-time laser-visual-inertial odometry and mapping,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.27811","last_updated":"2026-06-26T07:53:09Z","snapshot_observed_at":"2026-08-02T19:56:26.532905Z","submitted_at":"2026-06-26T07:53:09Z","title":"LXD-SLAM: LiDAR+X Dense SLAM with $\\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-06-29T04:38:21.101435Z"},"links":{"citing_paper":"/paper/2606.27811"},"observation_digest":"sha256:5201493b90a4c714e9e059547e7966e0bd36cd92952ea260b93137f7a1351d27","observation_id":"50709ecd-b683-418d-bf32-6bb5d96dbb5e","resolution":{"observed_at":"2026-06-29T04:38:21.101435Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T04:38:21.101435Z","title":"Direct lidar-inertial odometry: Lightweight LIO with continuous-time motion correction,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.27811","last_updated":"2026-06-26T07:53:09Z","snapshot_observed_at":"2026-08-02T19:56:26.532905Z","submitted_at":"2026-06-26T07:53:09Z","title":"LXD-SLAM: LiDAR+X Dense SLAM with $\\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-06-29T04:38:21.101435Z"},"links":{"citing_paper":"/paper/2606.27811"},"observation_digest":"sha256:b3d5423a0637e42ccbc13e94bdc54dbd5a2a833075745cf96402d0a37dfd12aa","observation_id":"492d10de-7c6d-4abb-9bde-68a4655bcadb","resolution":{"observed_at":"2026-06-29T04:38:21.101435Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T04:38:21.101435Z","title":"SR-LIVO: Lidar- inertial-visual odometry and mapping with sweep reconstruction,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.27811","last_updated":"2026-06-26T07:53:09Z","snapshot_observed_at":"2026-08-02T19:56:26.532905Z","submitted_at":"2026-06-26T07:53:09Z","title":"LXD-SLAM: LiDAR+X Dense SLAM with $\\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-06-29T04:38:21.101435Z"},"links":{"citing_paper":"/paper/2606.27811"},"observation_digest":"sha256:ed1491774fa1479f03d4251ef237894d843b7037b4db4256bf0c78960baa2e59","observation_id":"15d2bb73-7cf3-44e0-94ea-dd973f0e4556","resolution":{"observed_at":"2026-06-29T04:38:21.101435Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T04:38:21.101435Z","title":"Scan Context++: Structural place recogni- tion robust to rotation and lateral variations in urban environments,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.27811","last_updated":"2026-06-26T07:53:09Z","snapshot_observed_at":"2026-08-02T19:56:26.532905Z","submitted_at":"2026-06-26T07:53:09Z","title":"LXD-SLAM: LiDAR+X Dense SLAM with $\\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-06-29T04:38:21.101435Z"},"links":{"citing_paper":"/paper/2606.27811"},"observation_digest":"sha256:140e7c1b523f1f213849a7da05320186fa8394739d40fb7cc320c8ee89ce4048","observation_id":"9ecc9b52-2eab-401b-bec7-9e37142ffd85","resolution":{"observed_at":"2026-06-29T04:38:21.101435Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2606.27811","last_updated":"2026-06-26T07:53:09Z","latest_version":1,"primary_category":"cs.RO","snapshot_observed_at":"2026-08-02T19:56:26.532905Z","submitted_at":"2026-06-26T07:53:09Z","title":"LXD-SLAM: LiDAR+X Dense SLAM with $\\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations"},"reference_resolution":{"displayed":53,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":52,"verified_exact":1,"verified_fuzzy":0},"total_outbound_references":53},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 53 of 53 outbound references and 0 inbound Pith citation observations for arXiv:2606.27811."}