{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006559556,0.0001031194,0.0001352101,0.00004507547,0.00003099503,0.000007692784,0.00006633504,0.00006591144,0.0002309859],"category_scores_gemma":[0.00001122108,0.00009642005,0.00005808562,0.0001336921,0.00001703541,0.00005965286,0.000005942274,0.00003043163,0.000003920267],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001357816,"about_ca_system_score_gemma":0.00000675277,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001337145,"about_ca_topic_score_gemma":0.00001497412,"domain_scores_codex":[0.9993952,0.00001285739,0.0002321326,0.0001071821,0.00007580907,0.0001768006],"domain_scores_gemma":[0.999628,0.00002983267,0.00003082466,0.0001634928,0.0001041828,0.00004367089],"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":[0.00001475597,0.00002497937,0.0003875093,0.00005857763,0.00001027875,3.75932e-7,0.00004176087,0.9944393,0.0002046448,0.001061879,0.00249131,0.001264643],"study_design_scores_gemma":[0.0003080124,0.00009661,0.0004257837,0.00001343828,0.00001603057,2.561307e-7,0.00001072958,0.98823,0.0098736,0.0002992603,0.0006102709,0.0001160114],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003158793,0.00005307527,0.9939284,0.00000334942,0.0003367987,0.0003053566,0.000004181721,0.0001483728,0.002061637],"genre_scores_gemma":[0.9787713,0.000005207602,0.02076077,0.0001065773,0.00008555485,0.0000370776,0.00007290587,0.00003663544,0.0001240197],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9756125,"threshold_uncertainty_score":0.3931895,"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."}}