{"id":"W4205713395","doi":"10.23919/ecc54610.2021.9654924","title":"Two Key-Frame State Marginalization for Computationally Efficient Visual Inertial Navigation","year":2021,"lang":"en","type":"article","venue":"2021 European Control Conference (ECC)","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Science and Engineering Research Council","keywords":"Key (lock); Key frame; Frame (networking); Computer science; Inertial frame of reference; Computer vision; Artificial intelligence; Inertial navigation system; State (computer science); Reference frame; Position (finance); Inertial measurement unit; Real-time computing; Algorithm; Telecommunications","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":[0.0006783333,0.0008019644,0.0006591317,0.0005276937,0.0003761653,0.0008934614,0.0007950512,0.0006113406,0.003072497],"category_scores_gemma":[0.003943479,0.0002894143,0.0006123249,0.0004420465,0.0004167396,0.001242265,0.001069857,0.001020164,0.0007829275],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005099307,"about_ca_system_score_gemma":0.001044997,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007437189,"about_ca_topic_score_gemma":0.008593306,"domain_scores_codex":[0.9994634,0.0001021016,0.00002783287,0.0001196339,0.0002036942,0.0000832693],"domain_scores_gemma":[0.9992498,0.0002965895,0.00006726274,0.000152828,0.0002000531,0.00003354745],"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":[0.0009274565,0.0001600168,0.003382253,0.0002486991,0.0001680257,0.0001122346,0.000216612,0.2937318,0.03278938,0.02175099,0.004359296,0.6421533],"study_design_scores_gemma":[0.00002649967,0.00008145537,0.001403119,0.00001421293,0.00001740289,0.00006410846,0.0000368073,0.9780931,0.01251307,0.004420567,0.003308062,0.00002162679],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01220849,0.0001832499,0.9857724,0.00005391745,0.00004298675,0.00002808481,0.00009751075,0.0009881422,0.0006251193],"genre_scores_gemma":[0.4019629,0.0002468839,0.5925684,0.0001053533,0.00008834757,0.000147751,0.001435782,0.0003924876,0.00305209],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007437189,"threshold_uncertainty_score":0.01478779,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009976875157520161,"score_gpt":0.2312532627964322,"score_spread":0.2212763876389121,"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."}}