{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001516002,0.0004047334,0.0003072582,0.000299413,0.0001416529,0.0002190175,0.0001944961,0.0002885506,0.0003724399],"category_scores_gemma":[0.0002509941,0.0001105722,0.0001750904,0.0001991641,0.0001314514,0.000330611,0.0002174094,0.0001533862,0.0001416881],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001457612,"about_ca_system_score_gemma":0.0001993387,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003093519,"about_ca_topic_score_gemma":0.004351085,"domain_scores_codex":[0.9998884,0.00002500014,0.000003565114,0.00002723155,0.00003673209,0.00001902993],"domain_scores_gemma":[0.9998863,0.00002402112,0.00001750772,0.00001088137,0.00005183899,0.000009437997],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001129564,0.0002742421,0.02521185,0.000466913,0.0001214919,0.0008373568,0.000392676,0.1086573,0.5300255,0.0009903896,0.002269683,0.329623],"study_design_scores_gemma":[0.0001056478,0.00121558,0.05018684,0.00005010269,0.0001393198,0.0005166712,0.0002702734,0.7516817,0.189697,0.0004905035,0.005573114,0.00007328123],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7743722,0.000644681,0.220398,0.0001333117,0.0001929986,0.00003616648,0.0001569211,0.00118782,0.002877909],"genre_scores_gemma":[0.9744933,0.0001122774,0.0245054,0.00002388287,0.00001275533,0.000009878526,0.00006896681,0.000007282704,0.0007661948],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003093519,"threshold_uncertainty_score":0.006151021,"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."}}