{"id":"W1964074090","doi":"10.1108/02602280710731696","title":"Integration of vision and inertial sensors for industrial tools tracking","year":2007,"lang":"en","type":"article","venue":"Sensor Review","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer vision; Kalman filter; Artificial intelligence; Computer science; Orientation (vector space); Motion estimation; Machine vision; Tracking system; Inertial measurement unit; Tracking (education); 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004808031,0.0003741356,0.0003907609,0.0008619266,0.0001369875,0.0006939354,0.0006012179,0.0005430852,0.001148484],"category_scores_gemma":[0.0006460376,0.0002606311,0.000415637,0.0006860011,0.0002772205,0.001005539,0.0004560476,0.0003826075,0.0005066219],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003907817,"about_ca_system_score_gemma":0.0005148879,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001038709,"about_ca_topic_score_gemma":0.001074272,"domain_scores_codex":[0.9992803,0.00009809359,0.0000380942,0.0001459479,0.000401006,0.00003666696],"domain_scores_gemma":[0.9996469,0.00007575204,0.00005280917,0.00004112499,0.0001703402,0.00001316461],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00009321301,0.00007340078,0.00224965,0.0008455912,0.0001062672,0.0001301347,0.00007053259,0.0178346,0.09612105,0.008395931,0.002287327,0.8717924],"study_design_scores_gemma":[0.00005417432,0.001757387,0.02105132,0.0007493434,0.0005714179,0.001240159,0.0002480716,0.4665583,0.2613435,0.0152308,0.230983,0.0002125269],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02726329,0.03657772,0.9212642,0.0003929993,0.000514309,0.00007655318,0.00006188655,0.001234681,0.01261429],"genre_scores_gemma":[0.6638957,0.03004461,0.289416,0.0004643788,0.0004179108,0.0001279552,0.0002904454,0.000112308,0.01523074],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001148484,"threshold_uncertainty_score":0.003842115,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05750313494009128,"score_gpt":0.3013372877228379,"score_spread":0.2438341527827466,"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."}}