{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004513405,0.0001046822,0.0001320115,0.0003626909,0.00008214608,0.0003813151,0.0003279569,0.00004496785,0.00001025489],"category_scores_gemma":[0.00006279814,0.00006934699,0.00004006232,0.0002252496,0.00009758823,0.0001815289,0.00004819916,0.0004191866,0.000005775026],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001567224,"about_ca_system_score_gemma":0.0000858252,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002049097,"about_ca_topic_score_gemma":0.00002888869,"domain_scores_codex":[0.9987358,0.00006042745,0.0002890317,0.0001096411,0.0006285037,0.0001766205],"domain_scores_gemma":[0.9984758,0.0005160875,0.00004470949,0.000120452,0.0007829404,0.00006004584],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001195059,0.00003154835,0.00006516683,0.0001199462,0.0003805467,0.00006035735,0.001094626,0.9613361,0.01005979,0.00319829,0.01408927,0.009444789],"study_design_scores_gemma":[0.0003645866,0.0002657489,0.00008703386,0.0003610617,0.00002564157,0.0001612859,0.0008375766,0.9717202,0.001286504,0.0009351557,0.02385292,0.000102306],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1241714,0.003665968,0.8654637,0.003899264,0.001287852,0.0008070549,0.0004812736,0.00006170724,0.0001616968],"genre_scores_gemma":[0.9926642,0.0009160072,0.005560979,0.00004325523,0.0004117618,0.00001207297,0.0002698884,0.0000363489,0.00008551658],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8684927,"threshold_uncertainty_score":0.367703,"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."}}