{"id":"W3158014508","doi":"10.1109/icra48506.2021.9561150","title":"Invariant Extended Kalman Filtering Using Two Position Receivers for Extended Pose Estimation","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Kalman filter; Inertial measurement unit; Invariant extended Kalman filter; Multiplicative function; Control theory (sociology); Invariant (physics); Position (finance); Extended Kalman filter; Inertial frame of reference; Computer science; Ensemble Kalman filter; Mathematics; Algorithm; Artificial intelligence; Physics; Mathematical analysis","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":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0005430368,0.0004202071,0.0004555805,0.0002463823,0.0003749659,0.001178014,0.001092846,0.0003221062,0.00009605964],"category_scores_gemma":[0.0001153713,0.0004422568,0.000250678,0.0002773055,0.00003947818,0.0007843615,0.001632128,0.0005137436,0.00000853947],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002223293,"about_ca_system_score_gemma":0.0002372593,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002912184,"about_ca_topic_score_gemma":0.00003196123,"domain_scores_codex":[0.9969992,0.0001781752,0.0006215119,0.001285869,0.0004197062,0.0004955318],"domain_scores_gemma":[0.9974572,0.0002063474,0.0003866412,0.001457231,0.0003261237,0.0001664273],"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":[0.0001234857,0.0004537982,0.00001894615,0.0005800867,0.0002677302,0.0002965614,0.002053157,0.7298901,0.0261393,0.04413016,0.005325015,0.1907216],"study_design_scores_gemma":[0.0005511277,0.00004586171,0.0002501511,0.0005129335,0.00005489812,0.0001083396,0.00008126194,0.9837485,0.003955917,0.00998469,0.000154335,0.0005519965],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01492944,0.0001409059,0.9787258,0.000401299,0.003929241,0.000640987,0.00005131774,0.0005444523,0.0006365424],"genre_scores_gemma":[0.2313791,0.00004855696,0.7670652,0.0002853602,0.0003228206,0.00004427419,0.0007176659,0.00003404938,0.0001029949],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2538584,"threshold_uncertainty_score":0.9998589,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04012033989923865,"score_gpt":0.3052854895548219,"score_spread":0.2651651496555832,"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."}}