{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004308911,0.0008596411,0.000823041,0.001047889,0.0004391343,0.0008293272,0.0009237974,0.0006581132,0.002013274],"category_scores_gemma":[0.001401376,0.0004745267,0.000734737,0.001230752,0.0004839704,0.001302543,0.00125806,0.0008217648,0.001220707],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005188361,"about_ca_system_score_gemma":0.001155163,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006087641,"about_ca_topic_score_gemma":0.006749723,"domain_scores_codex":[0.9994763,0.00006901703,0.00002360702,0.0001600598,0.0001997303,0.00007142239],"domain_scores_gemma":[0.9994687,0.00006982034,0.00006873126,0.000194677,0.0001696336,0.00002836471],"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":[0.0002597343,0.0001178391,0.001002595,0.0001691862,0.00009097138,0.0001027627,0.0002366731,0.213696,0.1067183,0.005874165,0.003205651,0.6685261],"study_design_scores_gemma":[0.00003260317,0.0001349492,0.001220835,0.0000172852,0.0000241958,0.0001282375,0.00005856589,0.9570522,0.03212907,0.003967752,0.005194544,0.00003971169],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01141305,0.00006890279,0.9848866,0.00003574831,0.0000465976,0.00002091825,0.0000499957,0.002600147,0.0008779695],"genre_scores_gemma":[0.4246541,0.0001101583,0.5720479,0.00007312948,0.00004602271,0.00006184344,0.0003837681,0.0002601949,0.002362938],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006087641,"threshold_uncertainty_score":0.01210439,"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."}}