{"id":"W4388543840","doi":"10.1109/tie.2023.3327342","title":"Rumination Meets VSLAM: You Do Not Need to Build All the Submaps in Realtime","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Electronics","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Simultaneous localization and mapping; Computer science; Computer vision; Robustness (evolution); Artificial intelligence; Robot; 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":[0.0009660213,0.0008783169,0.0007824614,0.0002886428,0.000998441,0.001330596,0.001789963,0.0009954325,0.00252156],"category_scores_gemma":[0.003385068,0.0005034236,0.0005082365,0.000571491,0.001205573,0.003670358,0.003804565,0.001413677,0.001297595],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003059985,"about_ca_system_score_gemma":0.001331277,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001810066,"about_ca_topic_score_gemma":0.00259665,"domain_scores_codex":[0.9990288,0.0001714441,0.00005917141,0.0003258371,0.0002647906,0.0001500023],"domain_scores_gemma":[0.9980218,0.0003568688,0.0002503679,0.0009344344,0.0003024178,0.0001341666],"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":[0.001033925,0.0002410955,0.006570971,0.000881315,0.0001582373,0.001074246,0.002623056,0.09177457,0.1896446,0.03929795,0.008942756,0.6577573],"study_design_scores_gemma":[0.000166508,0.001059645,0.005824435,0.0001098898,0.0001231386,0.00195319,0.002003736,0.7060083,0.1446792,0.06663032,0.07125794,0.0001836365],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02466513,0.0002543087,0.9684411,0.0002398044,0.00006881094,0.00005417768,0.00006426773,0.002613961,0.003598432],"genre_scores_gemma":[0.5159087,0.0002545134,0.4784929,0.0003443494,0.00006136225,0.0001657736,0.0002908251,0.000402088,0.004079551],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00252156,"threshold_uncertainty_score":0.008435428,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02916679090157373,"score_gpt":0.2466592206766947,"score_spread":0.217492429775121,"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."}}