{"id":"W3112674834","doi":"10.1109/tnse.2020.3045263","title":"Global Visual and Semantic Observations for Outdoor Robot Localization","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Network Science and Engineering","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Key Research and Development Program of China; Natural Science Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Artificial intelligence; Computer vision; Computer science; Simultaneous localization and mapping; Robot; Gaussian process; Orb (optics); Landmark; Process (computing); Gaussian; Mobile robot; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006272664,0.0009019189,0.0008675969,0.001047559,0.0004068292,0.0009039268,0.0009175617,0.0008456914,0.002110993],"category_scores_gemma":[0.002427278,0.0004064537,0.0007068106,0.001607535,0.0008130469,0.002319802,0.00140706,0.001176117,0.001286607],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005134619,"about_ca_system_score_gemma":0.0009867314,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00789777,"about_ca_topic_score_gemma":0.01225138,"domain_scores_codex":[0.9993285,0.0001104576,0.00002850216,0.0002198798,0.0002333552,0.00007938163],"domain_scores_gemma":[0.9993229,0.0001453088,0.000106089,0.0002031441,0.0001897119,0.00003284798],"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":[0.0003818116,0.0000979123,0.005571734,0.0004039497,0.0001661543,0.0002346461,0.0003472749,0.1705447,0.03680025,0.03633876,0.009177812,0.739935],"study_design_scores_gemma":[0.00003568294,0.000128771,0.004977808,0.00005820991,0.00008813087,0.0002686949,0.0001470469,0.9151986,0.01799801,0.04226717,0.01876445,0.00006743901],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004328202,0.0002405248,0.9929859,0.0000646587,0.00003869713,0.00001262005,0.0001474054,0.001200738,0.0009812884],"genre_scores_gemma":[0.556685,0.0008437661,0.4361738,0.0002570087,0.0001686464,0.00009881963,0.001538999,0.0004306298,0.003803425],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00789777,"threshold_uncertainty_score":0.01570356,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0200176156381834,"score_gpt":0.2208409600972479,"score_spread":0.2008233444590645,"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."}}