{"id":"W4282601779","doi":"10.3390/s22124327","title":"An Integrated INS/LiDAR SLAM Navigation System for GNSS-Challenging Environments","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; Government of Ontario","keywords":"GNSS applications; Lidar; Extended Kalman filter; Mean squared error; Computer science; Kalman filter; Inertial navigation system; Simultaneous localization and mapping; Navigation system; Real-time computing; Trajectory; Remote sensing; Global Positioning System; Artificial intelligence; Geography; Telecommunications; Inertial frame of reference; Mobile robot; Mathematics; Robot","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001393597,0.0001350416,0.0001311378,0.00008959087,0.0002135569,0.00002779171,0.00009262108,0.00004978764,0.00001953172],"category_scores_gemma":[0.000006194645,0.0001528981,0.00004629836,0.0001345935,0.0000113346,0.00006468646,0.00001256035,0.0001299086,0.00001173505],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002885219,"about_ca_system_score_gemma":0.000006535823,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001474506,"about_ca_topic_score_gemma":0.000001518154,"domain_scores_codex":[0.9991678,0.00005162012,0.0002113493,0.0001815453,0.0001836212,0.0002040852],"domain_scores_gemma":[0.999667,0.00002467977,0.00003600323,0.0001978944,0.0000153032,0.00005906365],"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":[0.000009115673,0.00001803491,0.00004950688,0.0000604723,0.00002002173,0.00000732425,0.0004021858,0.9891091,0.008018706,0.0009478118,0.00003503872,0.001322685],"study_design_scores_gemma":[0.0003121278,0.0000760671,0.00007989576,0.00002183634,0.00001637056,0.00001016932,0.00243632,0.9847333,0.005985861,0.00002594691,0.006122794,0.0001793167],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8606253,0.00005834241,0.1375649,0.0000233785,0.0006070945,0.0003675086,0.00006630801,0.0003608906,0.0003262449],"genre_scores_gemma":[0.9982575,0.000006209532,0.001031042,0.00001160378,0.00006352401,0.00004543388,0.0004465567,0.00006188687,0.0000762776],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1376322,"threshold_uncertainty_score":0.6235003,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009340088308167203,"score_gpt":0.2058238406539291,"score_spread":0.1964837523457619,"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."}}