{"id":"W2904501409","doi":"10.1109/crv.2018.00034","title":"Robust UAV Visual Teach and Repeat Using Only Sparse Semantic Object Features","year":2018,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Computer vision; Computer science; Landmark; Robustness (evolution); Object (grammar); Cognitive neuroscience of visual object recognition; Object detection; Video tracking; Pattern recognition (psychology)","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":[0.00007352231,0.0001221603,0.000120159,0.00007472788,0.00008879763,0.00007072341,0.00003950586,0.00007751711,0.00005252655],"category_scores_gemma":[0.0000201665,0.0001090443,0.00002303208,0.0001144409,0.00004196611,0.00007639686,0.00001816405,0.00007994554,0.0000121016],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003022629,"about_ca_system_score_gemma":0.0000126205,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001128599,"about_ca_topic_score_gemma":0.0001691668,"domain_scores_codex":[0.9994144,0.00001603557,0.0001265365,0.0001578315,0.0001009378,0.0001841936],"domain_scores_gemma":[0.9997383,0.00001839287,0.00001372119,0.0001314574,0.0000388541,0.00005930442],"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":[0.00002527168,0.00006326961,0.01178375,0.0001729862,0.0001049782,0.000036781,0.0007458519,0.9297934,0.04243875,0.00178239,0.006490929,0.006561617],"study_design_scores_gemma":[0.0001828853,0.00005209676,0.003822377,0.00003230733,0.00002499695,0.00004629589,0.00008407387,0.9886876,0.006588263,0.00003013275,0.0002699012,0.0001791093],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.823358,0.0001282427,0.1710407,0.00002773504,0.0002605992,0.00009670161,0.000001051972,0.0002373692,0.004849574],"genre_scores_gemma":[0.9919948,0.00002250194,0.007168835,0.00006831163,0.0002222114,4.37369e-7,0.000007486486,0.00003189538,0.0004835412],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1686368,"threshold_uncertainty_score":0.4446699,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02112030928965343,"score_gpt":0.2403290946810926,"score_spread":0.2192087853914391,"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."}}