{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006789536,0.001487216,0.0008329569,0.001173398,0.0005391722,0.001056735,0.001042725,0.000874002,0.00348123],"category_scores_gemma":[0.004440881,0.0005697382,0.000735487,0.001491829,0.0006389515,0.001593958,0.002265737,0.001688001,0.002760921],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004689058,"about_ca_system_score_gemma":0.001036702,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002417861,"about_ca_topic_score_gemma":0.003146014,"domain_scores_codex":[0.9988074,0.0002210834,0.0000552079,0.0003138808,0.0005058005,0.00009655508],"domain_scores_gemma":[0.9986244,0.0001984703,0.0001748105,0.0004913316,0.0004613885,0.00004961051],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002192153,0.00006963177,0.002596889,0.0002956962,0.00009836542,0.0002211024,0.0003833831,0.3216514,0.04753916,0.02181358,0.007812949,0.5972986],"study_design_scores_gemma":[0.00001577314,0.00008000427,0.001361029,0.00003867857,0.00002232751,0.0003791392,0.00008523414,0.9536346,0.0226414,0.00905146,0.01264151,0.00004874494],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004475316,0.00008479133,0.9936871,0.00003337461,0.00003639925,0.00001279096,0.00007503168,0.0007047495,0.0008904642],"genre_scores_gemma":[0.2765516,0.0004295353,0.7159661,0.0001038527,0.00009369436,0.0001005232,0.001433825,0.0006613711,0.004659573],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00348123,"threshold_uncertainty_score":0.01164591,"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."}}