{"id":"W3025490466","doi":"10.3390/rs12101564","title":"Navigation Engine Design for Automated Driving Using INS/GNSS/3D LiDAR-SLAM and Integrity Assessment","year":2020,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":64,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Ministry of Science and Technology","keywords":"Lidar; GNSS applications; Computer science; Simultaneous localization and mapping; Odometry; Odometer; Robustness (evolution); Sensor fusion; Inertial navigation system; Inertial measurement unit; Global Positioning System; Satellite system; Ranging; Artificial intelligence; Real-time computing; Computer vision; Remote sensing; Robot; Mobile robot; Inertial frame of reference; Geography","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":[0.0001944566,0.0001766477,0.0002129922,0.00006046595,0.0001237234,0.0001078478,0.00003582437,0.0001163373,0.000001111711],"category_scores_gemma":[0.00009556234,0.0001945062,0.00003749876,0.0002122302,0.00001848922,0.0001208903,0.00001859189,0.0001981515,0.000001066361],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001358421,"about_ca_system_score_gemma":0.00003376189,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002002622,"about_ca_topic_score_gemma":0.000001960477,"domain_scores_codex":[0.9991142,0.00005177704,0.0002642895,0.0002114802,0.0001324636,0.000225826],"domain_scores_gemma":[0.9995348,0.0001098974,0.00005036704,0.0001056087,0.00009659842,0.0001026769],"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":[0.000004538555,0.000002522441,0.00001613658,0.0001240386,0.0000237539,0.000007327699,0.0002628216,0.8597387,0.1072469,0.00001953584,0.0000278569,0.03252586],"study_design_scores_gemma":[0.0003048,0.00003878833,0.00009906129,0.0002189324,0.00003995216,0.00001588451,0.00005617777,0.9871271,0.01173267,0.00007088565,0.00008575157,0.0002100141],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2912968,0.0000371608,0.707599,0.0001007838,0.0001331166,0.0002331772,0.000001565582,0.0005596832,0.00003876071],"genre_scores_gemma":[0.5986905,0.00001097462,0.4011028,0.0000413655,0.00009467608,2.142025e-8,0.00002152236,0.00003706585,0.000001082716],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.3073938,"threshold_uncertainty_score":0.7931731,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03994417171798145,"score_gpt":0.2792352074873567,"score_spread":0.2392910357693752,"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."}}