{"id":"W2943607143","doi":"10.1117/12.2521694","title":"Performance evaluation of neural network based integration of vision and motion sensors for vehicular navigation","year":2019,"lang":"en","type":"article","venue":"","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Military College of Canada; Queen's University","funders":"","keywords":"GNSS applications; Computer science; Air navigation; Gyroscope; Inertial navigation system; Odometry; Inertial measurement unit; Real-time computing; Multipath propagation; BeiDou Navigation Satellite System; Satellite system; Sensor fusion; Artificial intelligence; Computer vision; Global Positioning System; Orientation (vector space); Engineering; Telecommunications; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009818737,0.0009056584,0.0007060314,0.0004731629,0.0003000268,0.0005485333,0.0008181393,0.0008314997,0.001191745],"category_scores_gemma":[0.00200615,0.0002644075,0.0003096448,0.0004038034,0.0002345695,0.0006218271,0.0005263765,0.0005434702,0.0002877671],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008754548,"about_ca_system_score_gemma":0.0006331634,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02162394,"about_ca_topic_score_gemma":0.01352186,"domain_scores_codex":[0.9996056,0.00008268136,0.00002685787,0.00008279001,0.0001091319,0.00009304374],"domain_scores_gemma":[0.9992244,0.0002465498,0.00005716836,0.00003102705,0.0003984539,0.00004231756],"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":[0.001680125,0.0005970157,0.00801803,0.0002684229,0.0002158668,0.0001471331,0.00004734151,0.7854738,0.01114581,0.0004887267,0.0009091807,0.1910085],"study_design_scores_gemma":[0.00001176732,0.0002716802,0.001217098,0.000006255218,0.00002605998,0.00001247424,0.000009966112,0.9958994,0.002396201,0.0000553848,0.000087949,0.000005695061],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8276252,0.003576946,0.1557894,0.0003309828,0.0003632107,0.0001398845,0.0001663516,0.001780866,0.01022722],"genre_scores_gemma":[0.9904489,0.0002347171,0.007836746,0.00003513775,0.00001171389,0.00003439535,0.0001262802,0.00001248729,0.001259537],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02162394,"threshold_uncertainty_score":0.04299617,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01213039079144367,"score_gpt":0.2472964067281606,"score_spread":0.235166015936717,"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."}}