{"id":"W2011105647","doi":"10.1109/i2mtc.2012.6229318","title":"Obstacle detection for low flying UAS using monocular camera","year":2012,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Computer vision; Artificial intelligence; Extended Kalman filter; Computer science; Inertial navigation system; Elevation (ballistics); Kalman filter; Orientation (vector space); Position (finance); Terrain; Inertial measurement unit; Monocular; Obstacle; Trajectory; Digital elevation model; Image resolution; Filter (signal processing); Remote sensing; Geography; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.00006479958,0.00007429033,0.00007139786,0.00004436566,0.00008548101,0.00002388381,0.00002442394,0.00005204001,0.0000185941],"category_scores_gemma":[0.00001344251,0.00007604009,0.00003854049,0.00007982142,0.000004702976,0.0001487182,0.000004263862,0.00003602234,0.000009232182],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006572666,"about_ca_system_score_gemma":0.000002997089,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003264021,"about_ca_topic_score_gemma":0.000006312401,"domain_scores_codex":[0.9995661,0.000005920426,0.0001071832,0.00006142702,0.00005575651,0.0002036445],"domain_scores_gemma":[0.9998031,0.00001853102,0.00001157788,0.00008676893,0.00002652209,0.00005347375],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000002246849,0.00000938544,0.0003389245,0.0000463566,0.00001495311,1.696023e-7,0.0001125302,0.8836672,0.1015387,0.0002099695,0.00002917103,0.0140304],"study_design_scores_gemma":[0.0001141676,0.000006510526,0.000121069,0.0000067234,0.00001196652,0.000001975838,0.00005224879,0.8894815,0.1093719,0.00002700224,0.000707173,0.00009779093],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2903294,0.0001048921,0.7088606,0.000003267472,0.0003154954,0.00009980374,7.488579e-7,0.0001137724,0.0001719997],"genre_scores_gemma":[0.982091,0.000005842016,0.01763605,0.00002768976,0.0001580407,0.000006146753,0.00000559667,0.00003017227,0.00003949312],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6917616,"threshold_uncertainty_score":0.3100825,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02331392960046866,"score_gpt":0.2312068249110859,"score_spread":0.2078928953106173,"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."}}