{"id":"W1993352646","doi":"10.1016/s0924-2716(01)00030-2","title":"Digital image georeferencing from a multiple camera system by GPS/INS","year":2001,"lang":"en","type":"article","venue":"ISPRS Journal of Photogrammetry and Remote Sensing","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":69,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; University of Calgary","keywords":"Global Positioning System; Computer vision; Computer science; Differential GPS; Inertial navigation system; Artificial intelligence; Orientation (vector space); Trajectory; Calibration; Mobile mapping; Inertial measurement unit; Digital camera; Camera resectioning; Photogrammetry; Remote sensing; Geography; Mathematics; Point cloud","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":[0.0001425995,0.0002009366,0.0003379502,0.0001718867,0.0001069174,0.0002464993,0.00007117584,0.0001179383,0.000003744149],"category_scores_gemma":[0.0000682865,0.0001778681,0.0001112213,0.0002422634,0.00004563976,0.0002277429,0.00001663898,0.0003056715,0.000003187799],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000903524,"about_ca_system_score_gemma":0.00001457239,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000520054,"about_ca_topic_score_gemma":0.00005309597,"domain_scores_codex":[0.9988428,0.00003364002,0.0004861789,0.0001388371,0.0002229492,0.0002755361],"domain_scores_gemma":[0.9992727,0.0001255484,0.0001517022,0.0001283407,0.0001295783,0.0001921468],"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":[0.00008836636,0.00002977591,0.000648848,0.0001224565,0.0002394987,0.0006663826,0.000539946,0.02000296,0.2035287,0.000003967134,0.0005093101,0.7736198],"study_design_scores_gemma":[0.0008606372,0.00006273964,0.0001244194,0.0004331527,0.00006781412,0.0007403343,0.001234909,0.9742427,0.01889716,0.00004189532,0.003026515,0.0002676967],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5143321,0.0005276862,0.4840942,0.00001588681,0.0003046782,0.00004939278,0.00001263,0.00005232068,0.0006111569],"genre_scores_gemma":[0.9877905,0.00026285,0.01161006,0.00003086464,0.0002245992,9.597248e-9,0.00001791156,0.00004176821,0.00002143875],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9542398,"threshold_uncertainty_score":0.7253251,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00661981641496788,"score_gpt":0.1926583827815384,"score_spread":0.1860385663665705,"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."}}