{"id":"W2129373316","doi":"10.1109/iros.2001.973330","title":"Adaptive filtering for pose estimation in visual servoing","year":2002,"lang":"en","type":"article","venue":"","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":56,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Visual servoing; Kalman filter; Computer vision; Artificial intelligence; Computer science; Noise (video); Divergence (linguistics); Covariance matrix; Covariance; Tracking (education); Trajectory; Control theory (sociology); Robot; Mathematics; Algorithm; Image (mathematics); Control (management); Statistics","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":[0.00006664747,0.00005131854,0.00005657409,0.00007041734,0.00004460194,0.00005276192,0.0001412406,0.00001149216,0.00002689705],"category_scores_gemma":[0.00003012234,0.00004811753,0.00001795335,0.0001457155,0.000005174998,0.0007827402,0.00007135518,0.00003626955,0.0000269713],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002435316,"about_ca_system_score_gemma":0.000003063008,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004437643,"about_ca_topic_score_gemma":0.000003019686,"domain_scores_codex":[0.9995325,0.000008323383,0.0001031749,0.0001572132,0.0000640677,0.0001346891],"domain_scores_gemma":[0.9997797,0.00005476529,0.00002359041,0.00009503317,0.00002024619,0.0000266523],"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":[0.000003678826,0.00004488871,0.00006118153,0.000007617081,0.000001746971,0.000004277784,0.0008176232,0.007619794,0.00243817,0.0101568,0.0003486234,0.9784956],"study_design_scores_gemma":[0.0002161392,0.0000389266,0.0001968233,0.0000204204,2.831254e-7,0.000002810791,0.00003485977,0.9950713,0.00234035,0.001751486,0.0002591185,0.00006751787],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001832362,0.00002028523,0.9955749,0.0003975317,0.00008947356,0.00009536782,1.696005e-7,0.00008506833,0.001904873],"genre_scores_gemma":[0.4307987,0.000001475634,0.5687248,0.0002313412,0.000008809288,0.00000632199,2.406535e-7,0.000002583688,0.0002257782],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9874515,"threshold_uncertainty_score":0.1962176,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03439241172246655,"score_gpt":0.3057470000833345,"score_spread":0.271354588360868,"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."}}