{"id":"W4400822502","doi":"10.1177/02783649241261079","title":"YUTO MMS: A comprehensive SLAM dataset for urban mobile mapping with tilted LiDAR and panoramic camera integration","year":2024,"lang":"en","type":"article","venue":"The International Journal of Robotics Research","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mobile mapping; Inertial measurement unit; Global Positioning System; Benchmark (surveying); Simultaneous localization and mapping; Lidar; Computer science; Computer vision; Artificial intelligence; Data collection; Units of measurement; Synchronization (alternating current); Remote sensing; Geography; Cartography; Mobile robot; Telecommunications; Robot","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004481139,0.001479612,0.0008741349,0.001610147,0.0007977224,0.0007914316,0.001981882,0.001191482,0.004656396],"category_scores_gemma":[0.001792491,0.0004197235,0.0008610315,0.003096051,0.0004776264,0.0009603158,0.001776712,0.001185257,0.006612915],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007833689,"about_ca_system_score_gemma":0.001897237,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0456924,"about_ca_topic_score_gemma":0.1107317,"domain_scores_codex":[0.9992555,0.00009594808,0.0000669632,0.0002107787,0.0002505576,0.0001202884],"domain_scores_gemma":[0.9993229,0.00005930171,0.00005825119,0.0002215955,0.0002706705,0.00006733711],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000516975,0.0003761416,0.0173387,0.001849802,0.0003475513,0.0005235198,0.0004083019,0.02848298,0.01284902,0.002182868,0.8382437,0.0968805],"study_design_scores_gemma":[0.0004720063,0.0003371537,0.08706504,0.0007589026,0.0001484176,0.000762413,0.001631534,0.07971498,0.01454598,0.005067579,0.8091975,0.0002984775],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.04436309,0.00112166,0.02149019,0.0004882724,0.0003920258,0.0003228988,0.9128562,0.01236718,0.006598448],"genre_scores_gemma":[0.03168077,0.0001777547,0.01647588,0.00007982273,0.00002469006,0.0002050654,0.9500536,0.0003246795,0.0009777265],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0456924,"threshold_uncertainty_score":0.09085286,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05843168273399495,"score_gpt":0.3439202485181381,"score_spread":0.2854885657841431,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}