{"id":"W2947222761","doi":"10.5194/isprs-annals-iv-2-w5-187-2019","title":"ENHANCED UAV NAVIGATION USING HALL-MAGNETIC AND AIR-MASS FLOW SENSORS IN INDOOR ENVIRONMENT","year":2019,"lang":"en","type":"article","venue":"ISPRS annals of the photogrammetry, remote sensing and spatial information sciences","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"GNSS applications; Quadcopter; Inertial navigation system; Dead reckoning; Odometer; Real-time computing; Extended Kalman filter; Global Positioning System; Kalman filter; Computer science; Satellite system; Air navigation; Navigation system; Inertial measurement unit; SIGNAL (programming language); GNSS augmentation; Engineering; Inertial frame of reference; Aerospace engineering; Telecommunications; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004414591,0.0001332548,0.0001778732,0.00019301,0.000112106,0.00009132325,0.00007859267,0.00007525977,0.000004673671],"category_scores_gemma":[0.00004677885,0.0001075438,0.00003855756,0.0003538766,0.0001534877,0.000232084,0.00002998621,0.0001026209,0.000002729061],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001951298,"about_ca_system_score_gemma":0.00001602056,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01181615,"about_ca_topic_score_gemma":0.0004561992,"domain_scores_codex":[0.9989105,0.00004993453,0.0003861868,0.000124682,0.0003186924,0.0002099882],"domain_scores_gemma":[0.9995715,0.0000552441,0.0001296654,0.000139407,0.00005237908,0.00005181973],"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":[0.000006644224,0.000002660985,0.0002206289,0.00004560801,0.000003959747,1.352698e-7,0.0005610309,0.6341412,0.01211769,0.000001892371,0.000003220131,0.3528953],"study_design_scores_gemma":[0.0001912401,0.0000598126,0.00144431,0.0001266375,0.000005402634,0.00000416881,0.0004007395,0.9447992,0.05262443,0.0001382672,0.00007938992,0.0001263763],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6947545,0.00005983797,0.3044471,0.0001092655,0.0001738058,0.0002050858,0.000003816482,0.00001662734,0.000229866],"genre_scores_gemma":[0.9969172,0.000144146,0.002795458,0.0001120709,0.00001342612,9.138012e-8,0.000005415384,0.00000661208,0.000005527099],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3527689,"threshold_uncertainty_score":0.9947643,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0187132211117528,"score_gpt":0.2409242524343289,"score_spread":0.2222110313225761,"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."}}