{"id":"W1997062797","doi":"10.1109/iros.2005.1545120","title":"Concurrent mapping and localization for mobile robot using soft computing techniques","year":2005,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre For Cold Ocean Resources Engineering; Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Robustness (evolution); Mobile robot; Robot; Computer science; Fuzzy logic; Artificial intelligence; Genetic algorithm; Representation (politics); Fuzzy set; Set (abstract data type); Data mining; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00008444533,0.00009659123,0.00010718,0.00006649148,0.00008345293,0.00004596881,0.00003019572,0.00005510771,0.000007835616],"category_scores_gemma":[0.000008174875,0.0000994666,0.00002165197,0.00008413305,0.0000154881,0.00008255959,0.00001236528,0.00003718306,9.687725e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005330088,"about_ca_system_score_gemma":0.000005633468,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005662691,"about_ca_topic_score_gemma":0.000004767035,"domain_scores_codex":[0.9994867,0.000007113857,0.0001895387,0.0001132934,0.00005772787,0.0001456533],"domain_scores_gemma":[0.9997963,0.00003260177,0.00002303579,0.00006071764,0.00005038836,0.00003696614],"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":[4.010602e-7,0.000006244578,0.0001587155,0.00006352241,0.000005842977,9.20763e-8,0.00009415048,0.8992671,0.003488051,0.0004214417,0.000129302,0.09636516],"study_design_scores_gemma":[0.0001311567,0.00001537314,0.00002126977,0.0000460768,0.00000747385,0.000002501266,0.00005889052,0.9800641,0.01318159,0.00002460686,0.006317145,0.0001297994],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01255253,0.000197233,0.9862908,0.00001192037,0.00008585352,0.0003142701,0.000001724954,0.000357746,0.0001879522],"genre_scores_gemma":[0.9175062,0.00002888331,0.08218873,0.00007160717,0.0001374088,0.000007080041,0.00002037435,0.00002569666,0.00001405121],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9049537,"threshold_uncertainty_score":0.405613,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02037355997694802,"score_gpt":0.2544984034325547,"score_spread":0.2341248434556067,"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."}}