{"id":"W2967643980","doi":"10.1109/icra.2019.8793624","title":"Semantic Mapping for View-Invariant Relocalization","year":2019,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer vision; Artificial intelligence; Leverage (statistics); Scale-invariant feature transform; Computer science; Invariant (physics); Minimum bounding box; Simultaneous localization and mapping; Bounding overwatch; Robot; Mobile robot; Mathematics; Feature extraction; Image (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":[0.00007938463,0.00007919473,0.000101857,0.00005727041,0.00002581835,0.00002777646,0.00004567687,0.0000537154,0.0001015461],"category_scores_gemma":[0.00001291589,0.00007491069,0.00003361086,0.0001242154,0.000003854082,0.00006262823,0.000005558188,0.00002878656,0.0001240851],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002724827,"about_ca_system_score_gemma":0.000006316793,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006674964,"about_ca_topic_score_gemma":0.000005045159,"domain_scores_codex":[0.9995217,0.000006565473,0.0001583515,0.0001047672,0.00006987654,0.0001387318],"domain_scores_gemma":[0.9997571,0.00002715228,0.00001103631,0.0001350641,0.00004010612,0.00002954312],"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":[0.000001616477,0.000005498493,0.0002967352,0.0001771071,0.00001283373,3.359264e-7,0.00005550036,0.9723561,0.005129409,0.01993166,0.00116933,0.0008638771],"study_design_scores_gemma":[0.0002289565,0.00001513931,0.0002352061,0.00003188703,0.000005947371,0.000001063114,0.00003163335,0.9869851,0.001865161,0.0003493944,0.01013398,0.0001165264],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01751012,0.00005065324,0.9767983,0.00007658924,0.000375387,0.0003234636,0.00000153118,0.0002367051,0.004627254],"genre_scores_gemma":[0.9920388,0.00003750984,0.006768581,0.0001637533,0.00005926193,0.000005749281,0.00005302122,0.00003506686,0.0008382239],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9745287,"threshold_uncertainty_score":0.3054769,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01094030158447295,"score_gpt":0.1945396536513234,"score_spread":0.1835993520668504,"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."}}