{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005301412,0.0001757316,0.0001753499,0.00004858922,0.00008457298,0.00009664439,0.0002432444,0.00008980258,0.000104577],"category_scores_gemma":[0.00001702812,0.0001793583,0.00006601577,0.0003167999,0.00003252578,0.0001472638,0.00001690489,0.0001930111,0.00006263559],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004169282,"about_ca_system_score_gemma":0.0000291039,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001031267,"about_ca_topic_score_gemma":0.000006928295,"domain_scores_codex":[0.9989579,0.000034281,0.0002252756,0.0002222028,0.0002788678,0.0002815146],"domain_scores_gemma":[0.9995395,0.00008721584,0.00003843017,0.0001582664,0.00004705213,0.0001295588],"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":[0.00003304087,0.00001508924,0.00274449,0.00004630402,0.00001627437,0.00001632762,0.00001924197,0.9825331,0.0002553272,0.0002443318,0.01377838,0.0002980854],"study_design_scores_gemma":[0.0004676961,0.0000442215,0.004839961,0.00003287626,0.00001601322,0.000001914284,0.0000023931,0.9926655,0.0003591088,0.00007621687,0.001287367,0.000206772],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1325767,0.0001651327,0.8585828,0.003871171,0.00207363,0.0004316344,0.00002194926,0.000692281,0.001584638],"genre_scores_gemma":[0.9944907,0.000009890655,0.0007646274,0.003661752,0.0009247428,0.00001064777,0.0000745818,0.0000435808,0.00001942727],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.861914,"threshold_uncertainty_score":0.7314019,"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."}}