{"id":"W4296107532","doi":"10.3390/robotics11050091","title":"Improved Visual SLAM Using Semantic Segmentation and Layout Estimation","year":2022,"lang":"en","type":"article","venue":"Robotics","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Computer vision; Computer science; Visual odometry; Simultaneous localization and mapping; Segmentation; Trajectory; Representation (politics); Pose; Robot; Set (abstract data type); Cuboid; Mobile robot; Mathematics","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":[0.0000749293,0.00008919306,0.00008974814,0.00007134148,0.0001574027,0.00004197024,0.0000328897,0.00002782497,0.0000167925],"category_scores_gemma":[0.000009495139,0.0001067775,0.00001723864,0.0001116448,0.00001099747,0.00007941657,0.0000284336,0.0000876754,0.000001821031],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001014742,"about_ca_system_score_gemma":0.00001142962,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001746271,"about_ca_topic_score_gemma":0.000003913682,"domain_scores_codex":[0.9994656,0.00002416279,0.0001558754,0.0001013857,0.000125397,0.0001276003],"domain_scores_gemma":[0.9998223,0.00001972727,0.00003021959,0.00007293408,0.00002023479,0.00003457997],"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":[0.000002362271,0.00001277861,0.0002578577,0.00004113902,0.00001129362,0.000001897944,0.0001880286,0.985095,0.01235042,0.0002642839,0.00002192848,0.001752988],"study_design_scores_gemma":[0.0002212913,0.00004024719,0.0001753331,0.000005782507,0.00003291289,0.00001003574,0.0001188299,0.997847,0.001320996,0.0000914241,0.00001477395,0.0001213458],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2301674,0.00006126212,0.7691806,0.00002976314,0.0002763813,0.0001341686,0.000003523386,0.0001074655,0.00003944335],"genre_scores_gemma":[0.9693074,0.000009561176,0.03047861,0.00003684672,0.00003427754,0.000004295982,0.00006889575,0.0000297864,0.00003029362],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.73914,"threshold_uncertainty_score":0.4354259,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01188512956939968,"score_gpt":0.2400182283827274,"score_spread":0.2281330988133277,"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."}}