{"id":"W4413894140","doi":"10.5194/isprs-archives-xlviii-m-8-2025-43-2025","title":"State Covariance Based Spatio-Temporal Trajectory Alignment for VIO Systems","year":2025,"lang":"en","type":"article","venue":"The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Covariance; Trajectory; State (computer science); Covariance intersection; Computer science; Covariance function; Algorithm; Mathematics; Physics; Statistics","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":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001953411,0.0004481929,0.0004827308,0.000989934,0.001622479,0.001398275,0.002738727,0.0001007077,0.000003534859],"category_scores_gemma":[0.0006102068,0.0002930979,0.0003974678,0.001162122,0.002095926,0.0007004954,0.0009901334,0.0003982116,0.000002009115],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006782809,"about_ca_system_score_gemma":0.0004535428,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.6574336,"about_ca_topic_score_gemma":0.1063349,"domain_scores_codex":[0.995085,0.0003721075,0.001655261,0.0005405727,0.001758901,0.0005881627],"domain_scores_gemma":[0.9952106,0.001767382,0.001713448,0.0007359307,0.0004211335,0.0001514993],"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":[0.0001741676,0.00002626999,0.0001923658,0.00007364415,0.0000885929,2.903079e-7,0.001499303,0.03125474,0.000571124,0.00007467758,0.0002343033,0.9658105],"study_design_scores_gemma":[0.001005564,0.0001333149,0.001278806,0.0005279868,0.00003568534,0.0000436206,0.0006604513,0.9682508,0.003995093,0.006778865,0.01697712,0.0003126888],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00187379,0.00006007708,0.9843202,0.004650307,0.004264283,0.001016957,0.0001966084,0.00008226591,0.003535568],"genre_scores_gemma":[0.9809675,0.0001043751,0.01686964,0.001698264,0.0001221868,0.00000130101,0.00007046119,0.00001016841,0.000156109],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9790937,"threshold_uncertainty_score":0.9999521,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01679570633418926,"score_gpt":0.2558528322853083,"score_spread":0.2390571259511191,"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."}}