{"id":"W3161130397","doi":"10.1109/mim.2021.9436097","title":"A Modern Solution for an Old Calibration Problem","year":2021,"lang":"en","type":"article","venue":"IEEE Instrumentation & Measurement Magazine","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Computer vision; Artificial intelligence; Visual servoing; Robot calibration; Coordinate system; Robot end effector; Robot; Camera auto-calibration; Cartesian coordinate robot; Transformation matrix; Computer science; Robot kinematics; Frame (networking); Transformation (genetics); Camera resectioning; 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":[0.003210529,0.001382192,0.0009370258,0.001437106,0.002116928,0.002817902,0.002811113,0.004239046,0.02754528],"category_scores_gemma":[0.01105464,0.0006930105,0.001059876,0.001794309,0.003808584,0.005898479,0.005036341,0.006066811,0.01115117],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001245385,"about_ca_system_score_gemma":0.002050866,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001585379,"about_ca_topic_score_gemma":0.001218835,"domain_scores_codex":[0.9965604,0.0007243872,0.0001825908,0.001115944,0.001215503,0.0002010686],"domain_scores_gemma":[0.9969773,0.0009538141,0.0001880248,0.0009138761,0.0008709704,0.00009602623],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00008598757,0.00005447276,0.0004175693,0.0004418932,0.00006707098,0.0002154737,0.0003798076,0.008521729,0.001578224,0.6477055,0.07431105,0.2662212],"study_design_scores_gemma":[0.0000739564,0.00007393511,0.0003835247,0.0003434397,0.00003676215,0.0008646784,0.0002106918,0.02986061,0.002365972,0.5578851,0.4078168,0.00008447922],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003025463,0.007148862,0.9083655,0.01559762,0.003207117,0.00006944464,0.0002657346,0.0009090661,0.06141114],"genre_scores_gemma":[0.1231577,0.01570553,0.7078764,0.01125172,0.007268905,0.000522761,0.001066402,0.001066629,0.132084],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02754528,"threshold_uncertainty_score":0.09214813,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05026403560852995,"score_gpt":0.2481568963169223,"score_spread":0.1978928607083923,"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."}}