{"id":"W2889216088","doi":"10.1109/ccece.2018.8447685","title":"Mobile Robot Motion Tracking Using Descriptor Matching and Sensor Fusion","year":2018,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Computer vision; Artificial intelligence; Computer science; Mobile robot; Kalman filter; Odometry; Extended Kalman filter; Visual odometry; Feature (linguistics); Scale-invariant feature transform; Sensor fusion; Robot; Feature extraction","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":[0.00007941286,0.0001045943,0.00009546979,0.00006949395,0.0001223644,0.00007486851,0.00002981371,0.00006604873,0.00007050997],"category_scores_gemma":[0.000006757651,0.0001010505,0.00001975515,0.00009674778,0.00002379566,0.0001594229,0.00001391321,0.00005860416,0.00001299172],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004667761,"about_ca_system_score_gemma":0.000003365085,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005695046,"about_ca_topic_score_gemma":0.00001603338,"domain_scores_codex":[0.9994549,0.00001489284,0.0001487431,0.0001304936,0.00009095741,0.0001600722],"domain_scores_gemma":[0.9997711,0.00001153417,0.00001656867,0.00009874332,0.00004918619,0.00005293261],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000003328838,0.000009382467,0.0003394268,0.00003830574,0.000008021592,0.000001880744,0.0005746149,0.6279893,0.3571599,0.0002441323,0.00004164967,0.01359001],"study_design_scores_gemma":[0.0001440917,0.0000350079,0.0004466747,0.00003484265,0.0000116765,0.00001147109,0.0002241291,0.956391,0.04222496,0.000161295,0.0001731067,0.0001417914],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4847252,0.00004048593,0.5143307,0.000003573392,0.0001985966,0.00006939645,6.094292e-7,0.0001336653,0.000497775],"genre_scores_gemma":[0.9706771,0.00002499194,0.02897742,0.00003393132,0.0001844122,0.00000143141,0.000004963137,0.00003084921,0.00006486212],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4859519,"threshold_uncertainty_score":0.4120718,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02212018171252251,"score_gpt":0.2340756059540154,"score_spread":0.2119554242414929,"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."}}