{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000642949,0.001324498,0.0009683748,0.0005093201,0.0005647272,0.0009094247,0.0008482615,0.00114676,0.001985654],"category_scores_gemma":[0.001798274,0.0006073965,0.0008061819,0.0007603448,0.0006129877,0.0009993793,0.001692859,0.001249154,0.0006560598],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004044225,"about_ca_system_score_gemma":0.001150753,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001946074,"about_ca_topic_score_gemma":0.002256349,"domain_scores_codex":[0.9989384,0.0001985505,0.00003971137,0.0003012312,0.000447367,0.00007477878],"domain_scores_gemma":[0.999561,0.0001075078,0.00008784726,0.0001118347,0.0001112182,0.00002061947],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001835875,0.00008824313,0.001736882,0.0003043011,0.0001277753,0.0002228811,0.0003068024,0.5277779,0.06930377,0.01352487,0.001953942,0.384469],"study_design_scores_gemma":[0.00001498735,0.0001235063,0.001023265,0.00001989209,0.00003121075,0.0002634086,0.00006446119,0.9522678,0.03671453,0.004713078,0.004723004,0.00004080105],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005043477,0.00007772439,0.9934488,0.00002447694,0.00001670103,0.00001550484,0.0000089216,0.000155233,0.001209201],"genre_scores_gemma":[0.4272818,0.0002496654,0.5684685,0.00005702319,0.00004458944,0.00009251913,0.00007892553,0.0001438592,0.003583156],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001985654,"threshold_uncertainty_score":0.006642699,"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."}}