{"id":"W2903713534","doi":"10.1109/inertialsensors.2018.8577143","title":"Visual lnertial Hybridization Technique based on Beacons identified by Deep Learning","year":2018,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Safran Electronics (Canada)","funders":"","keywords":"Beacon; Artificial intelligence; Computer vision; Computer science; Sight; Point (geometry); Line-of-sight; Visualization; Line (geometry); Position (finance); Sensor fusion; Real-time computing; Engineering; Aerospace engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007114374,0.0008274131,0.0007240173,0.001193966,0.000298702,0.0005898657,0.001419406,0.0007981988,0.002438135],"category_scores_gemma":[0.002001511,0.0004033639,0.0005218961,0.000881833,0.0005368929,0.001042905,0.001693085,0.001128295,0.0007834096],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006163031,"about_ca_system_score_gemma":0.0004850737,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002378486,"about_ca_topic_score_gemma":0.003068235,"domain_scores_codex":[0.9994891,0.00008041511,0.00001788161,0.0001458911,0.0001813289,0.000085373],"domain_scores_gemma":[0.9993569,0.0001624113,0.00008794809,0.0001369345,0.0002141647,0.0000415896],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000435164,0.0001988558,0.001600703,0.000116947,0.0001031757,0.0001285381,0.0002101716,0.09361753,0.09591812,0.006226108,0.002511573,0.7989331],"study_design_scores_gemma":[0.00003328934,0.00023038,0.0009971564,0.000015907,0.00003858726,0.0001543758,0.00004091692,0.9515375,0.04164005,0.00237915,0.00291204,0.00002065173],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02369632,0.0001686972,0.9726959,0.0001091904,0.00008889988,0.00003474477,0.00003436796,0.001516429,0.001655572],"genre_scores_gemma":[0.5485916,0.0001797419,0.445154,0.0002213622,0.00006523787,0.000100294,0.0002155916,0.0001815988,0.005290535],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002438135,"threshold_uncertainty_score":0.008156359,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004070944475012695,"score_gpt":0.2142290435174909,"score_spread":0.2101580990424783,"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."}}