{"id":"W3171825621","doi":"10.1007/978-981-16-3264-8_19","title":"2D Autonomous Robot Localization Using Fast SLAM 2.0 and YOLO in Long Corridors","year":2021,"lang":"en","type":"book-chapter","venue":"Smart innovation, systems and technologies","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Robot; Simultaneous localization and mapping; Artificial intelligence; Computer science; Autonomous robot; Computer vision; Environmental science; Mobile robot","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":[0.0001960254,0.0005448031,0.0004669453,0.0004302282,0.0002975848,0.0007017229,0.0005702864,0.0005402553,0.003390318],"category_scores_gemma":[0.0002867969,0.0005161994,0.0004622754,0.00082955,0.0003782618,0.0009201913,0.001041524,0.0006585621,0.001842466],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003632202,"about_ca_system_score_gemma":0.0004525725,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00624517,"about_ca_topic_score_gemma":0.01175433,"domain_scores_codex":[0.9998711,0.00002227924,0.000005044166,0.00002864269,0.00005059359,0.00002242302],"domain_scores_gemma":[0.9999496,0.00001775679,0.000004556869,0.000008796479,0.0000137931,0.000005544072],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001803384,0.00003817195,0.001275975,0.0004426465,0.00005262039,0.000303112,0.000436683,0.1919014,0.02420803,0.04194678,0.03159573,0.7076185],"study_design_scores_gemma":[0.00003005823,0.000165313,0.003140767,0.0001771834,0.00003282258,0.0006529184,0.0002811813,0.7997143,0.01146355,0.0233682,0.1608788,0.00009493635],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01206665,0.005160968,0.9629273,0.0002207798,0.0003207854,0.00003068489,0.0002338629,0.003403054,0.01563592],"genre_scores_gemma":[0.2209679,0.006085365,0.7049078,0.0001724408,0.0001624326,0.0001590773,0.001227314,0.0009284398,0.06538922],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00624517,"threshold_uncertainty_score":0.01241761,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01898664618648552,"score_gpt":0.2103392625792329,"score_spread":0.1913526163927474,"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."}}