{"id":"W2979561114","doi":"10.1109/ccece.2019.8861901","title":"Construction of Autonomous Driving Maps employing LiDAR Odometry","year":2019,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Odometry; Lidar; Inertial measurement unit; Computer science; Artificial intelligence; Computer vision; Global Positioning System; Remote sensing; Simultaneous localization and mapping; Ranging; Feature (linguistics); Visual odometry; Geography; Mobile robot; Robot","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005200446,0.0000696524,0.0001162067,0.0001106958,0.00001530629,0.00001477768,0.00004361408,0.00005406528,0.0001903773],"category_scores_gemma":[0.00001057465,0.00007088625,0.00003277269,0.0001557372,0.00001311267,0.00007228384,0.000009532853,0.00005483391,0.00006000283],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003209057,"about_ca_system_score_gemma":0.000007880228,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001077556,"about_ca_topic_score_gemma":0.000002306445,"domain_scores_codex":[0.9995497,0.000007123155,0.0001725161,0.0000812228,0.00008279247,0.000106644],"domain_scores_gemma":[0.9997602,0.00002946527,0.00002345526,0.0001298491,0.00002934345,0.00002767265],"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":[0.000002395259,0.0000131998,0.08540818,0.0001649367,0.00004319616,0.000001243119,0.0001192602,0.8433007,0.04705017,0.0154456,0.0001775231,0.008273593],"study_design_scores_gemma":[0.0005760605,0.00006964248,0.01066322,0.00009759796,0.00002347473,0.00001608642,0.0003592081,0.8906842,0.09443404,0.0006581349,0.002039543,0.000378838],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8325768,0.00003056991,0.1517837,0.00001123548,0.0004715477,0.00008060922,0.000001402225,0.0001963453,0.01484788],"genre_scores_gemma":[0.9875736,0.00001123523,0.0121783,0.000009932057,0.00002662325,6.612045e-7,0.000007316798,0.00001932478,0.0001730676],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1549968,"threshold_uncertainty_score":0.2890657,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005190985167748396,"score_gpt":0.1841249248689124,"score_spread":0.178933939701164,"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."}}