{"id":"W2823329554","doi":"10.1139/juvs-2018-0007","title":"A method for UAV multi-sensor fusion 3D-localization under degraded or denied GPS situation","year":2018,"lang":"en","type":"article","venue":"Journal of Unmanned Vehicle Systems","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Global Positioning System; Inertial measurement unit; Computer science; GPS signals; Offset (computer science); Real-time computing; Sensor fusion; Kalman filter; Computer vision; Artificial intelligence; Extended Kalman filter; Smoothing; Remote sensing; Assisted GPS; Geography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003311375,0.0006193494,0.0006130963,0.0006176002,0.0005049974,0.0004969756,0.000771958,0.0006402602,0.001018169],"category_scores_gemma":[0.0007955104,0.0002695583,0.0006532248,0.0006019931,0.0003609895,0.0007234894,0.0009244741,0.0006418775,0.0004154429],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003822766,"about_ca_system_score_gemma":0.0007170803,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003163037,"about_ca_topic_score_gemma":0.002773625,"domain_scores_codex":[0.999542,0.00005725518,0.00002865489,0.0001515128,0.0001811176,0.00003959518],"domain_scores_gemma":[0.9997754,0.00004097759,0.00003434715,0.00004127197,0.00009527965,0.00001286808],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002210011,0.00005876791,0.002059479,0.0002216174,0.0001107901,0.0002848626,0.0003717595,0.1734933,0.07241382,0.01041358,0.002373271,0.7379779],"study_design_scores_gemma":[0.00001645542,0.00009437742,0.0009465798,0.00001301844,0.00003657716,0.0002452696,0.00005275275,0.9739578,0.01850454,0.002086778,0.004013811,0.00003201985],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003716526,0.00009330078,0.9954738,0.000020384,0.00002532892,0.00001797546,0.00001029187,0.0002858554,0.0003564969],"genre_scores_gemma":[0.331108,0.0002853471,0.6652662,0.00006306425,0.00005445336,0.0001196186,0.0001373076,0.00008000393,0.002886067],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003163037,"threshold_uncertainty_score":0.006289244,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04376655644780911,"score_gpt":0.2984797386908496,"score_spread":0.2547131822430405,"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."}}