{"id":"W3188511888","doi":"10.1109/tmech.2021.3103995","title":"Simultaneous Hand–Eye/Robot–World/Camera–IMU Calibration","year":2021,"lang":"en","type":"article","venue":"IEEE/ASME Transactions on Mechatronics","topic":"Optical measurement and interference techniques","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Inertial measurement unit; Artificial intelligence; Computer vision; Calibration; Computer science; Robot; Robot calibration; Inertial frame of reference; Camera resectioning; Mathematics; Robot kinematics; Mobile robot","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002298514,0.000287096,0.0002775964,0.0002162764,0.0003570788,0.0004409621,0.0006236895,0.0001508758,0.0003223095],"category_scores_gemma":[0.00002866131,0.0002854255,0.0002052006,0.0007591504,0.00006788979,0.0006976959,0.00001269936,0.0005372991,0.0001626777],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002063228,"about_ca_system_score_gemma":0.0002470407,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001615545,"about_ca_topic_score_gemma":0.0001929587,"domain_scores_codex":[0.9977947,0.0001370571,0.0004043467,0.0006303294,0.0005475072,0.0004860975],"domain_scores_gemma":[0.9984995,0.0001706162,0.00008318049,0.0008077095,0.0002629108,0.0001761444],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001467032,0.002328733,0.00002442926,0.0001227833,0.000341231,0.0002005767,0.001124112,0.09847751,0.4043855,0.1693432,0.001695746,0.3218095],"study_design_scores_gemma":[0.0003764655,0.0003797666,0.00000314113,0.00009824184,0.00003631046,0.00001281737,0.00003759386,0.3075198,0.6809869,0.00527683,0.004859558,0.0004125749],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001424541,0.0001440855,0.9922599,0.002564486,0.001001695,0.000226636,0.0000115686,0.0006270302,0.001740058],"genre_scores_gemma":[0.9315363,0.0001420192,0.06540491,0.0006368209,0.00007084216,0.00004612364,0.000006830259,0.00002707562,0.002129054],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9301118,"threshold_uncertainty_score":0.9999598,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0249237393984306,"score_gpt":0.26717201603373,"score_spread":0.2422482766352994,"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."}}