{"id":"W2331269236","doi":"10.7210/jrsj.22.83","title":"Indoor Navigation based on an Inaccurate Map using Object Recognition","year":2004,"lang":"en","type":"article","venue":"Journal of the Robotics Society of Japan","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Pan African Materials Institute; Canadian Institute for Advanced Research","keywords":"Artificial intelligence; Computer vision; Mobile robot navigation; Mobile robot; Computer science; Robot; Path (computing); Object (grammar); Motion planning; Representation (politics); Desk; Dead reckoning; Robot control; Global Positioning System","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003750134,0.0005682086,0.0006481502,0.0006802907,0.0003502312,0.0006787413,0.0006966837,0.0005046028,0.0006578261],"category_scores_gemma":[0.000867945,0.0004188803,0.000395617,0.0006173622,0.0005683662,0.001389542,0.0008238036,0.0004291139,0.0005491313],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002824864,"about_ca_system_score_gemma":0.0004613457,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00314009,"about_ca_topic_score_gemma":0.002441082,"domain_scores_codex":[0.99964,0.00007543389,0.00001295633,0.00007002764,0.0001594193,0.00004208695],"domain_scores_gemma":[0.9995794,0.0001049512,0.00006864054,0.0001133109,0.0001131874,0.00002050748],"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":[0.0004652658,0.0001071868,0.006564989,0.0002348138,0.0001376714,0.001128714,0.0004438793,0.1639061,0.2086819,0.009959103,0.002561387,0.6058089],"study_design_scores_gemma":[0.00003407846,0.0002029504,0.005535264,0.00003354305,0.0001365736,0.0008942448,0.0001101205,0.8530858,0.1251652,0.005871235,0.008836336,0.00009467088],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04414922,0.0002193367,0.9523135,0.00005580515,0.00005194572,0.00001379631,0.00002898161,0.002076093,0.001091238],"genre_scores_gemma":[0.5280975,0.0003803857,0.4687211,0.00006301617,0.00003036802,0.00004117465,0.0001589481,0.0001171794,0.002390255],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00314009,"threshold_uncertainty_score":0.006243646,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02663472612089588,"score_gpt":0.2451092419935825,"score_spread":0.2184745158726867,"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."}}