{"id":"W2318452820","doi":"10.7210/jrsj.20.425","title":"Mobile Robot Localization on a Map with Large Inaccuracy.","year":2002,"lang":"en","type":"article","venue":"Journal of the Robotics Society of Japan","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institute for Advanced Research","keywords":"Mobile robot; Global Map; Robot; Artificial intelligence; Computer vision; Computer science; Mobile robot navigation; Reference frame; Frame (networking); Process (computing); Topological map; Monte Carlo localization; Simultaneous localization and mapping; Frame of reference; Robot control","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001862303,0.0001468765,0.0002499179,0.00004129574,0.00009883371,0.00002889533,0.0002282757,0.00009910419,0.00003349701],"category_scores_gemma":[0.0000181548,0.00009917438,0.0002394834,0.0002516164,0.00005375539,0.0001070235,0.00002048949,0.0002408982,0.00000501726],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001117429,"about_ca_system_score_gemma":0.00001566367,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001628923,"about_ca_topic_score_gemma":0.000002641557,"domain_scores_codex":[0.9988822,0.00003331967,0.0004217013,0.0000753632,0.0003940828,0.000193365],"domain_scores_gemma":[0.9991846,0.00005167558,0.000275779,0.0002298215,0.0001910204,0.00006709983],"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":[0.000007648017,0.0001159424,0.001041705,0.00008931773,0.00009792544,5.261887e-7,0.001027998,0.9838277,0.0003362498,0.0002483018,0.01296891,0.0002377377],"study_design_scores_gemma":[0.000716934,0.0003491005,0.0002433775,0.0002314375,0.00007918431,0.00001894447,0.0004655872,0.9939955,0.00150498,0.0001025554,0.002152658,0.0001396787],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03586363,0.0009157946,0.9605581,0.0009215485,0.0008264435,0.0003196286,0.000008379986,0.0000574099,0.0005290494],"genre_scores_gemma":[0.9894862,0.0003796614,0.009505281,0.0002217274,0.0001410919,0.000001056857,0.000002053945,0.00004144726,0.0002214637],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9536226,"threshold_uncertainty_score":0.4044214,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01104943477216085,"score_gpt":0.2005308029500255,"score_spread":0.1894813681778646,"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."}}