{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001048291,0.0001234767,0.0001130009,0.00004870032,0.0002122484,0.00009813744,0.00006179811,0.0000481586,0.000001457317],"category_scores_gemma":[0.00001249756,0.0001330481,0.00002182911,0.0007838127,0.00005210749,0.0002247592,0.000001200736,0.00006098272,9.12606e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004610377,"about_ca_system_score_gemma":0.00002430499,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003784617,"about_ca_topic_score_gemma":0.000007646096,"domain_scores_codex":[0.9992486,0.000003374162,0.0001491967,0.0001990774,0.0001508936,0.0002489026],"domain_scores_gemma":[0.9996739,0.00003484813,0.00001171288,0.00006256902,0.00006450195,0.0001524842],"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":[0.00000406102,0.000005246188,0.00006959262,0.00005393735,0.00000795469,3.634924e-7,0.00006566288,0.9950269,0.001160986,0.0003064155,0.00008261825,0.003216195],"study_design_scores_gemma":[0.000176907,0.00005525488,0.0005375454,0.00002972857,0.00002120222,0.000002257486,0.00002144257,0.9980477,0.0005548181,0.00002180882,0.0003851945,0.0001461399],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01800299,0.00008354298,0.9809429,0.000185339,0.0003760826,0.0001779199,0.000007599935,0.000207053,0.00001656326],"genre_scores_gemma":[0.9938959,0.000102992,0.005664344,0.0001976145,0.00009934347,0.00001649738,0.000002988849,0.00001711853,0.000003207705],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9758929,"threshold_uncertainty_score":0.5425544,"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."}}