{"id":"W1581129594","doi":"10.1109/crv.2015.11","title":"The Battle for Filter Supremacy: A Comparative Study of the Multi-State Constraint Kalman Filter and the Sliding Window Filter","year":2015,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute for Christian Studies; University of Toronto","funders":"","keywords":"Kalman filter; Computer science; Filter (signal processing); Computer vision; Artificial intelligence; Inertial measurement unit; Feature (linguistics); Extended Kalman filter; Consistency (knowledge bases)","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003803513,0.00014708,0.0002040686,0.00002189865,0.0001654461,0.00008151833,0.0001765963,0.00002900572,0.00001260556],"category_scores_gemma":[0.0001110438,0.00006529805,0.00005890869,0.00008078834,0.0001554724,0.00005723185,0.00005677369,0.0001038342,0.000002731096],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002127501,"about_ca_system_score_gemma":0.00001427099,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005040027,"about_ca_topic_score_gemma":0.0003536421,"domain_scores_codex":[0.9991339,0.0001180185,0.0002738035,0.0001324139,0.0001540155,0.0001878475],"domain_scores_gemma":[0.9985663,0.000932865,0.00005328899,0.0002871925,0.0001130398,0.00004732914],"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":[0.000324447,0.0002344589,0.005490548,0.00007403878,0.000559094,0.000001927466,0.05702634,0.913465,0.001792833,0.005700768,0.01425634,0.001074173],"study_design_scores_gemma":[0.004464496,0.000102829,0.00190835,0.00002250974,0.00005162436,0.000003311983,0.008288515,0.9788163,0.00467382,0.0002374988,0.001277375,0.0001533688],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8615358,0.0001643458,0.1327717,0.000775583,0.0008929311,0.002312775,0.00004646955,0.00008055502,0.001419791],"genre_scores_gemma":[0.9988645,0.000005455394,0.0006637254,0.00005773719,0.00003272678,0.00004944393,0.000003842716,0.00001827891,0.0003043201],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1373286,"threshold_uncertainty_score":0.2662777,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07219013997929272,"score_gpt":0.2753111870398979,"score_spread":0.2031210470606052,"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."}}