{"id":"W1949754115","doi":"10.1109/robot.1999.774048","title":"Sensor fusion based on fuzzy Kalman filtering for autonomous robot vehicle","year":2003,"lang":"en","type":"article","venue":"","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":98,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Extended Kalman filter; Kalman filter; Sensor fusion; Fuzzy logic; Computer science; Fast Kalman filter; Fuse (electrical); Inertial navigation system; Control theory (sociology); Computer vision; Mobile robot; Artificial intelligence; Global Positioning System; Noise (video); GPS/INS; Invariant extended Kalman filter; Control engineering; Engineering; Robot; Orientation (vector space); Assisted GPS; Mathematics; Control (management)","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":[0.00007353449,0.0001087447,0.00009344859,0.00004920108,0.0000693758,0.00001951784,0.00003851487,0.00006234804,0.0001180284],"category_scores_gemma":[0.00002236429,0.0001027624,0.00005417993,0.00007548398,0.000005603752,0.00004860849,0.000002872729,0.00006209865,0.00005345044],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005789376,"about_ca_system_score_gemma":0.000005368863,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001321602,"about_ca_topic_score_gemma":0.000008158288,"domain_scores_codex":[0.9994575,0.00001209218,0.0001319775,0.0001213084,0.00007652373,0.0002006098],"domain_scores_gemma":[0.999733,0.00004938354,0.00001149332,0.00013503,0.00002198979,0.00004914875],"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":[0.00002549616,0.00002724239,0.00007230784,0.00005042236,0.000006896107,0.000002753778,0.00004963396,0.6056526,0.385811,0.001455241,0.0006293094,0.006217037],"study_design_scores_gemma":[0.0005286496,0.00008369696,0.0004836656,0.00001623468,0.000006783539,0.000001272503,0.00001413676,0.5830653,0.404972,0.00008829978,0.01056462,0.0001753828],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7294936,0.00003208666,0.1485977,0.0001654235,0.0009711282,0.0006532129,0.00001403888,0.001087522,0.1189853],"genre_scores_gemma":[0.9913193,0.000001549207,0.007852885,0.0001371667,0.00007788565,0.00001835006,0.00001531233,0.00003167372,0.0005458847],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2618257,"threshold_uncertainty_score":0.4190529,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01281291740017078,"score_gpt":0.2196924341742066,"score_spread":0.2068795167740358,"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."}}