{"id":"W4389356461","doi":"10.5194/isprs-annals-x-1-w1-2023-643-2023","title":"LIDAR SLAM-AIDED VEHICULAR NAVIGATION SYSTEM FOR GNSS-DENIED ENVIRONMENTS","year":2023,"lang":"en","type":"article","venue":"ISPRS annals of the photogrammetry, remote sensing and spatial information sciences","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"GNSS applications; Satellite system; Computer science; Extended Kalman filter; Inertial navigation system; Navigation system; Kalman filter; GNSS augmentation; Lidar; Global Positioning System; Simultaneous localization and mapping; Real-time computing; Air navigation; Remote sensing; Artificial intelligence; Geography; Telecommunications; Inertial frame of reference; Mobile robot; Robot","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":[0.0003398556,0.0005894495,0.000499219,0.0007045016,0.0003256789,0.0004481535,0.000826853,0.0004272519,0.001905537],"category_scores_gemma":[0.0006200068,0.0002335608,0.0002938266,0.0005988543,0.0001338799,0.0004973687,0.0008827611,0.0004642232,0.00189645],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002624502,"about_ca_system_score_gemma":0.0009055811,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006911558,"about_ca_topic_score_gemma":0.01131308,"domain_scores_codex":[0.9997943,0.00003501884,0.00001124034,0.00004718758,0.00007873351,0.00003349379],"domain_scores_gemma":[0.9997603,0.00001488044,0.00002112176,0.00004976623,0.0001378169,0.00001617662],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006789581,0.0002360327,0.01624949,0.000380796,0.0002745073,0.0005660389,0.0003847794,0.1082289,0.1585774,0.001915366,0.02012742,0.6923802],"study_design_scores_gemma":[0.000136949,0.0003243125,0.01266264,0.00004487755,0.00008827947,0.0003489998,0.0002229526,0.9270052,0.04102165,0.001170275,0.01690742,0.00006648931],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2972814,0.0007234042,0.6707591,0.0003182601,0.0004495271,0.0002153735,0.002037267,0.0196217,0.008593936],"genre_scores_gemma":[0.8663277,0.0001252124,0.1268857,0.000125563,0.00003972272,0.0001243736,0.002604947,0.00009473314,0.003671909],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006911558,"threshold_uncertainty_score":0.01374263,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03474853143465324,"score_gpt":0.2679324022931782,"score_spread":0.233183870858525,"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."}}