{"id":"W2120809655","doi":"10.1109/wcica.2011.5970654","title":"Neural network based extended Kalman filter for localization of mobile robots","year":2011,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Extended Kalman filter; Odometry; Invariant extended Kalman filter; Computer science; Mobile robot; Computer vision; Kalman filter; Covariance; Artificial intelligence; Covariance intersection; Fast Kalman filter; Covariance matrix; Robot; Divergence (linguistics); Noise (video); Algorithm; Mathematics","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":[0.0003674378,0.0004210261,0.0004989026,0.0003721187,0.0002331163,0.0004117847,0.0005523097,0.0005995238,0.001218756],"category_scores_gemma":[0.001150327,0.0002025498,0.0003392502,0.0005194175,0.0002567077,0.0008067122,0.0003550106,0.0005761083,0.0003680114],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000527355,"about_ca_system_score_gemma":0.0004694065,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01058663,"about_ca_topic_score_gemma":0.006876665,"domain_scores_codex":[0.9997465,0.00005845545,0.0000180848,0.00006195856,0.00009081022,0.00002411007],"domain_scores_gemma":[0.999762,0.0000956365,0.00003026331,0.00001306895,0.00009328915,0.000005736669],"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":[0.00009398033,0.00002734071,0.0009390297,0.0001956965,0.0000752578,0.0001121263,0.00007190531,0.7884665,0.005530744,0.01185493,0.00148274,0.1911499],"study_design_scores_gemma":[0.00000523735,0.00001696174,0.000226399,0.000008933332,0.00001065297,0.00001834629,0.000004117302,0.995862,0.00070383,0.001787196,0.001349345,0.000007018061],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004243949,0.001223361,0.9928134,0.00006439519,0.00007394648,0.000009273485,0.00002288578,0.0002745002,0.001274415],"genre_scores_gemma":[0.7373891,0.004280198,0.245741,0.0001793769,0.0002790653,0.0002119572,0.0003057748,0.00008599691,0.0115275],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01058663,"threshold_uncertainty_score":0.02104998,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02312088791194043,"score_gpt":0.2168522648624109,"score_spread":0.1937313769504705,"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."}}