{"id":"W4383109007","doi":"10.1109/icra48891.2023.10160505","title":"Extrinsic calibration for highly accurate trajectories reconstruction","year":2023,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Université Laval","keywords":"Computer science; GNSS applications; Artificial intelligence; Context (archaeology); Calibration; Computer vision; Ground truth; Robotics; Total station; Position (finance); Software deployment; Real-time computing; Robot; Remote sensing; Global Positioning System; Geodesy; Geography; Mathematics; Telecommunications","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":[0.00004645106,0.00007182045,0.00007299666,0.0001297955,0.00006183668,0.00003427523,0.00005393286,0.00008089164,0.0000306398],"category_scores_gemma":[0.00004052644,0.00006664894,0.00003053745,0.000375104,0.00001953144,0.000211022,0.000006956372,0.0000368569,0.00003180691],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000234238,"about_ca_system_score_gemma":0.000007389201,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003588653,"about_ca_topic_score_gemma":0.00001628596,"domain_scores_codex":[0.9995986,0.000003580386,0.0001288052,0.00008520154,0.00004723272,0.0001365905],"domain_scores_gemma":[0.9998174,0.00003848138,0.00001179457,0.0000898037,0.00002814314,0.0000144045],"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":[0.00004516175,0.00001569821,0.002165806,0.0004576766,0.0001300723,0.000004913288,0.001022699,0.1618566,0.04740377,0.2375771,0.09646408,0.4528565],"study_design_scores_gemma":[0.0004083577,0.00003792434,0.0007536509,0.00001254959,0.00001104578,0.000005643636,0.001042651,0.5862181,0.3907297,0.008611337,0.01190599,0.00026309],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3041974,0.00005963521,0.6764054,0.0003512323,0.002425073,0.0004405838,0.0000291995,0.01238108,0.003710384],"genre_scores_gemma":[0.997722,0.00007409292,0.001348549,0.00001539408,0.00008175606,0.00006997645,0.00004419527,0.00001969104,0.0006243188],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6935247,"threshold_uncertainty_score":0.2717865,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01950094285859293,"score_gpt":0.2299674620121674,"score_spread":0.2104665191535745,"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."}}