{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000680654,0.0001286806,0.0001698076,0.0001969889,0.000315886,0.0001494617,0.0001320361,0.00007867037,6.26535e-7],"category_scores_gemma":[0.0001035361,0.00009996366,0.0000901873,0.0006148828,0.0001314583,0.0001949758,0.00003234458,0.00006492597,0.000006481014],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001876406,"about_ca_system_score_gemma":0.00001555319,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005486186,"about_ca_topic_score_gemma":0.00017582,"domain_scores_codex":[0.9988101,0.00003743572,0.0004149859,0.0001118066,0.000384575,0.0002411348],"domain_scores_gemma":[0.9994517,0.00007564114,0.0001649395,0.0001657333,0.00008506256,0.00005695275],"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":[0.00001359609,0.000003362626,0.00005094986,0.0002193998,0.00002732919,4.541415e-7,0.0006535503,0.4588801,0.007885392,0.00002820536,0.0002873644,0.5319503],"study_design_scores_gemma":[0.0001632236,0.00004193066,0.0004083576,0.0001270036,0.0000114611,0.000004603523,0.0005105818,0.9205623,0.07644144,0.0001707885,0.001446924,0.0001114255],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2902147,0.00002830369,0.7082069,0.0002699062,0.0005446788,0.0003610344,0.00001981788,0.0001156496,0.0002390096],"genre_scores_gemma":[0.9990765,0.00004903671,0.0007017624,0.00007147133,0.00003789305,4.92779e-7,0.00003865724,0.000009322178,0.00001484779],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7088618,"threshold_uncertainty_score":0.8293508,"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."}}