{"id":"W2153600745","doi":"10.5194/isprsarchives-xl-1-w4-329-2015","title":"A FEASIBILITY STUDY ON USING ViSP’S 3D MODEL-BASED TRACKER FOR UAV POSE ESTIMATION IN OUTDOOR ENVIRONMENTS","year":2015,"lang":"en","type":"article","venue":"˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; York University","keywords":"Computer vision; Artificial intelligence; Orientation (vector space); Computer science; Tracking (education); Pose; Visual servoing; Image (mathematics); Mathematics","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":[0.00130036,0.0004125591,0.0002896468,0.0003145909,0.0003268297,0.0005236019,0.0005862559,0.0005102326,0.002001089],"category_scores_gemma":[0.002507303,0.0002128695,0.0002298904,0.0002745269,0.0002890983,0.001208252,0.0005741458,0.0002687028,0.0004912555],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002737932,"about_ca_system_score_gemma":0.0005661473,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002204193,"about_ca_topic_score_gemma":0.002086621,"domain_scores_codex":[0.9994954,0.0001826142,0.00001711403,0.0001083238,0.0001542404,0.00004218167],"domain_scores_gemma":[0.9987695,0.0004892852,0.00007421699,0.0001755603,0.000400075,0.00009146239],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002465878,0.001041296,0.04131297,0.0005056509,0.0001137758,0.001987735,0.0008414974,0.08569404,0.4496988,0.003775441,0.002696304,0.4098667],"study_design_scores_gemma":[0.0002634267,0.009369032,0.02574834,0.00005766008,0.00008371461,0.001331862,0.0006585525,0.745126,0.2041851,0.001000836,0.01210509,0.00007027708],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5481392,0.0002041601,0.4432855,0.0003334877,0.0001274098,0.0003909688,0.0001640816,0.0009942607,0.006361008],"genre_scores_gemma":[0.8940408,0.00008494463,0.1044158,0.00003831325,0.00001344845,0.00007698472,0.0001671284,0.00003451643,0.00112809],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002204193,"threshold_uncertainty_score":0.006877065,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04068320616311615,"score_gpt":0.285603505474708,"score_spread":0.2449202993115918,"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."}}