{"id":"W2833205538","doi":"10.23919/icins.2018.8405844","title":"Robust IMU/UWB integration for indoor pedestrian navigation","year":2018,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Extended Kalman filter; Inertial measurement unit; Kalman filter; Computer science; Inertial navigation system; Covariance intersection; Covariance; Sensor fusion; Invariant extended Kalman filter; Computer vision; Control theory (sociology); Orientation (vector space); Artificial intelligence; Mathematics; Statistics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.000402671,0.000722195,0.0007095899,0.0005918691,0.0002716074,0.0005247504,0.0006180865,0.000542069,0.00112449],"category_scores_gemma":[0.0007680817,0.0002684529,0.0005176673,0.000571948,0.0001850912,0.0005276403,0.0007846858,0.0003844159,0.0009191149],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002113067,"about_ca_system_score_gemma":0.0004634276,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001675289,"about_ca_topic_score_gemma":0.002049369,"domain_scores_codex":[0.9996185,0.00007862408,0.00001721646,0.0001036326,0.0001291615,0.0000528132],"domain_scores_gemma":[0.9998023,0.0000232156,0.00002992455,0.0000390018,0.00009209554,0.00001345275],"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":[0.0007404095,0.0001012849,0.00847452,0.0004130108,0.0001977868,0.000680421,0.0003128762,0.09522003,0.1009769,0.009732926,0.005896013,0.7772539],"study_design_scores_gemma":[0.00003710783,0.0004719504,0.006581665,0.00006431592,0.0001946628,0.0008385654,0.0001662748,0.8468695,0.1209637,0.004389044,0.01933652,0.00008671259],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02111465,0.0005489208,0.9744248,0.00005294385,0.0001322108,0.00002130342,0.00008223276,0.001586119,0.002036805],"genre_scores_gemma":[0.7544863,0.0004750969,0.2394064,0.00008729386,0.00009748685,0.00009011108,0.0003648801,0.00008539735,0.004907083],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001675289,"threshold_uncertainty_score":0.003761828,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02752925102077252,"score_gpt":0.2368336969249711,"score_spread":0.2093044459041986,"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."}}