{"id":"W4226486514","doi":"10.22215/etd/2021-14888","title":"Multi-Sensor Fusion for Navigation of Ground Vehicles","year":2021,"lang":"en","type":"dissertation","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Global Positioning System; GPS/INS; Visual odometry; Inertial navigation system; Extended Kalman filter; Odometry; Computer science; Computer vision; Sensor fusion; Navigation system; Artificial intelligence; Kalman filter; Assisted GPS; Inertial measurement unit; Inertial frame of reference; Robot; Mobile robot; Telecommunications","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":[0.00005225036,0.0001623985,0.0002323798,0.00008168961,0.00003872688,0.00002627412,0.00005528606,0.0002602059,0.00003605753],"category_scores_gemma":[0.00003273383,0.0001689022,0.00009325367,0.0001456202,0.00000632195,0.00004552197,0.000003105188,0.00008331914,0.000003792268],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003613563,"about_ca_system_score_gemma":0.00002551628,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005140302,"about_ca_topic_score_gemma":0.0001301438,"domain_scores_codex":[0.9992517,0.00001091626,0.0003220408,0.0001584843,0.0001389316,0.0001179875],"domain_scores_gemma":[0.9993861,0.00005080725,0.00007099684,0.0001437415,0.0003173496,0.00003096706],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005394661,0.0001068775,0.00007580211,0.004294967,0.0001403913,0.000004018291,0.001038406,0.4056307,0.5715055,0.001398125,0.0009138084,0.01483742],"study_design_scores_gemma":[0.0005314999,0.00003625141,0.001564816,0.0003888876,0.0000812541,8.767382e-7,0.00119843,0.8407028,0.1544348,0.00007461071,0.0006787598,0.0003070329],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8197113,0.0007507502,0.1753896,0.00001180938,0.001599652,0.000653412,0.00006252853,0.0002085365,0.00161249],"genre_scores_gemma":[0.8901817,0.0004377364,0.06566153,0.00002289675,0.0002560806,0.00006975688,0.03026086,0.0002176403,0.0128918],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4350721,"threshold_uncertainty_score":0.6887633,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01783709957506079,"score_gpt":0.2570515595725584,"score_spread":0.2392144599974976,"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."}}