{"id":"W2928277903","doi":"10.3390/ijgi8040169","title":"Enhanced Drone Navigation in GNSS Denied Environment Using VDM and Hall Effect Sensor","year":2019,"lang":"en","type":"article","venue":"ISPRS International Journal of Geo-Information","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Drone; GNSS applications; Heading (navigation); Inertial navigation system; Global Positioning System; Computer science; Extended Kalman filter; Real-time computing; Odometer; Kalman filter; Air navigation; Engineering; Artificial intelligence; Aerospace engineering; Telecommunications; Inertial frame of reference","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":[0.0002669334,0.0001142277,0.000156902,0.00029845,0.00002065328,0.00009083044,0.0001017909,0.00007627684,0.00003304743],"category_scores_gemma":[0.00003233586,0.0001111663,0.00004547913,0.00006408746,0.00001334801,0.001404262,0.00001775449,0.000154742,0.00004517586],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002865366,"about_ca_system_score_gemma":0.00001752136,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001975324,"about_ca_topic_score_gemma":0.000001258437,"domain_scores_codex":[0.9988734,0.00002868114,0.0005399118,0.00004996716,0.0003938953,0.0001142149],"domain_scores_gemma":[0.9994848,0.0000506195,0.0002231277,0.00006618769,0.0001295553,0.0000456512],"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":[0.00008509008,0.0000109159,0.001641207,0.00005062607,0.00004980163,0.000004205231,0.0005614563,0.962107,0.02412654,0.0001659558,0.0000184424,0.01117874],"study_design_scores_gemma":[0.002181514,0.0001324817,0.007850819,0.0002640996,0.00002237844,0.00009824758,0.0001729497,0.9574032,0.03068857,0.0001273275,0.0008619664,0.0001963837],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8349842,0.00003972178,0.1638414,0.00005701807,0.0006825181,0.0001385098,0.000005286094,0.00001258361,0.0002387216],"genre_scores_gemma":[0.9978195,0.0001144682,0.001876379,0.00005104599,0.00007066346,0.000001330172,0.00004923564,0.000008846866,0.000008472349],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1628354,"threshold_uncertainty_score":0.4533231,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002784679413188419,"score_gpt":0.1983999054086139,"score_spread":0.1956152259954255,"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."}}