{"id":"W3010889738","doi":"10.1109/access.2020.2981648","title":"Indoor 3D Semantic Robot VSLAM Based on Mask Regional Convolutional Neural Network","year":2020,"lang":"en","type":"article","venue":"IEEE Access","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Natural Science Research of Jiangsu Higher Education Institutions of China; Natural Science Foundation of Jiangsu Province; Government of Jiangsu Province; Changzhou Institute of Technology; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; RANSAC; Convolutional neural network; Computer vision; Semantic feature; Simultaneous localization and mapping; Feature (linguistics); Object (grammar); Pose; Robot; Position (finance); Pattern recognition (psychology); Image (mathematics); 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":[0.0002370569,0.0007673223,0.0005060606,0.0005831629,0.0003506983,0.0005241015,0.001112657,0.0005162804,0.001726628],"category_scores_gemma":[0.0004645818,0.0003853675,0.0005595274,0.0005113313,0.0003715836,0.0008290198,0.0008911386,0.000495221,0.0006363286],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006424592,"about_ca_system_score_gemma":0.001096302,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01206329,"about_ca_topic_score_gemma":0.01258482,"domain_scores_codex":[0.9997745,0.00001959137,0.000008714679,0.00008092714,0.00007488421,0.00004141304],"domain_scores_gemma":[0.9998516,0.00001804542,0.00001979895,0.0000374703,0.00006164948,0.00001148214],"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":[0.0001926983,0.0000660168,0.001442389,0.0000989362,0.00006756939,0.0001140722,0.0001293649,0.267401,0.02949644,0.004805138,0.004262207,0.6919242],"study_design_scores_gemma":[0.000007232389,0.00004867194,0.0006731293,0.000006353854,0.00001420154,0.00006229393,0.00002145099,0.9847901,0.0106477,0.001542074,0.002171362,0.00001541316],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02330779,0.0001319623,0.9700133,0.00007077396,0.00004318568,0.00003691057,0.0001308875,0.004163489,0.002101696],"genre_scores_gemma":[0.5649288,0.0002062139,0.4289941,0.000133677,0.00002743348,0.000126658,0.0007032003,0.000189424,0.004690452],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01206329,"threshold_uncertainty_score":0.02398616,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03996370240526784,"score_gpt":0.2452795184096684,"score_spread":0.2053158160044006,"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."}}