{"id":"W2765227569","doi":"10.1109/cpgps.2017.8075140","title":"Loosely coupled visual odometry aided inertial navigation system using discrete extended Kalman filter with pairwise time correlated measurements","year":2017,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Kalman filter; Odometry; Covariance; Computer science; Extended Kalman filter; Cholesky decomposition; Invariant extended Kalman filter; Covariance intersection; Algorithm; Covariance matrix; Pairwise comparison; Artificial intelligence; Fast Kalman filter; Noise (video); Filter (signal processing); Computer vision; Control theory (sociology); Mathematics; Statistics; Eigenvalues and eigenvectors","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":[0.0002072771,0.0002594808,0.0002821392,0.0001133277,0.0003175718,0.0002290288,0.0001682592,0.0001422135,0.00006534011],"category_scores_gemma":[0.00003342497,0.0002149788,0.00005579726,0.0001364376,0.00005011997,0.0003523513,0.00002970249,0.0001386022,0.00006831794],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002013909,"about_ca_system_score_gemma":0.00002548735,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001154646,"about_ca_topic_score_gemma":0.000009172933,"domain_scores_codex":[0.9986134,0.000043008,0.0003577999,0.0002559314,0.0004303128,0.0002995484],"domain_scores_gemma":[0.9991477,0.00001963592,0.0001391576,0.0004037161,0.000165228,0.0001246108],"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":[0.000151731,0.00004692348,0.00265915,0.0002003472,0.000218791,0.00004591858,0.00009146315,0.8408028,0.1552058,0.00007898942,0.0001151154,0.000382908],"study_design_scores_gemma":[0.001437111,0.00008394059,0.002464784,0.0003251646,0.00009634502,0.00001790587,0.00003449459,0.9805593,0.01465374,0.000003114877,0.000006771521,0.0003172494],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7351197,0.00002054867,0.262827,0.00001125189,0.000312855,0.000327003,0.000004905923,0.0003952267,0.0009815064],"genre_scores_gemma":[0.9972288,0.000001042033,0.002235736,0.00001119578,0.00009427784,0.000006232741,0.0001235488,0.00007529828,0.0002238952],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2621091,"threshold_uncertainty_score":0.876658,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02286742539170058,"score_gpt":0.2471245302661255,"score_spread":0.2242571048744249,"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."}}