{"id":"W4243010224","doi":"10.1109/iembs.2006.4397445","title":"Comparing Unscented and Extended Kalman Filter Algorithms in the Rigid-Body Point-Based Registration","year":2006,"lang":"en","type":"article","venue":"Conference proceedings","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Extended Kalman filter; Fiducial marker; Computer science; Algorithm; Kalman filter; Computer vision; Artificial intelligence; Image registration; Filter (signal processing); Unscented transform; Invariant extended Kalman filter; Image (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":[0.0001595009,0.000139181,0.0001318201,0.00008991195,0.0000633354,0.0001960583,0.0001148879,0.00006507136,0.00001028067],"category_scores_gemma":[0.00001839128,0.0001172194,0.00001945063,0.0001866869,0.00004788514,0.0001654089,0.000008220191,0.0001428492,0.000003531906],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003367174,"about_ca_system_score_gemma":0.00001394433,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008913025,"about_ca_topic_score_gemma":0.00005318669,"domain_scores_codex":[0.9992394,0.000007260945,0.0002276491,0.0001756383,0.0001542126,0.0001958359],"domain_scores_gemma":[0.9997394,0.00002072279,0.00004255225,0.00007599789,0.00009184686,0.00002945912],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001993587,0.0008769023,0.2748869,0.002016196,0.00008443312,0.00007364288,0.007543888,0.1505996,0.1618021,0.3715418,0.02034561,0.01002955],"study_design_scores_gemma":[0.0004761763,0.00003008232,0.03615662,0.00008067944,0.000009529437,0.00000512772,0.0002489829,0.9577679,0.003457693,0.001300099,0.0003076777,0.0001593987],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8789362,0.00008832398,0.1067209,0.0006654517,0.0001037973,0.0004279796,0.000003118448,0.0002451814,0.01280903],"genre_scores_gemma":[0.9988231,0.00001056443,0.0009323633,0.00007466517,0.00005424327,0.00001655071,0.00003771795,0.00001490498,0.00003589957],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8071684,"threshold_uncertainty_score":0.4780069,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02015449706295143,"score_gpt":0.2221951018311665,"score_spread":0.202040604768215,"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."}}