{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001179148,0.0004214697,0.0003749329,0.0003556868,0.000166253,0.0002975094,0.0003629182,0.000357781,0.0005322886],"category_scores_gemma":[0.0002467355,0.0001445515,0.0002389658,0.0002679016,0.0001965721,0.0004975826,0.0005734283,0.0003314252,0.0002605827],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001296807,"about_ca_system_score_gemma":0.0002364578,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001925536,"about_ca_topic_score_gemma":0.003453719,"domain_scores_codex":[0.9998497,0.0000248255,0.000005188529,0.0000385548,0.00006438483,0.00001733106],"domain_scores_gemma":[0.9999286,0.00001386505,0.0000109938,0.00001434507,0.00002521735,0.000006976801],"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":[0.0004982132,0.000129635,0.006637473,0.0004136324,0.0001180642,0.000621143,0.0004315658,0.1243891,0.3902645,0.004417259,0.001989917,0.4700896],"study_design_scores_gemma":[0.0001021464,0.0006881166,0.008684958,0.00006384845,0.00008874577,0.0007898748,0.0002238549,0.8825678,0.09190916,0.001739767,0.01305193,0.00008989924],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2007666,0.001108755,0.7880762,0.000214053,0.0002583358,0.00005656655,0.0001244099,0.001121551,0.008273526],"genre_scores_gemma":[0.8785363,0.0004522064,0.1169756,0.00007063094,0.00006632273,0.00003353695,0.0001392853,0.0000211579,0.003705061],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001925536,"threshold_uncertainty_score":0.003828645,"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."}}