{"id":"W4402803804","doi":"10.1109/tac.2024.3466874","title":"PEBO-SLAM: Observer Design for Visual Inertial SLAM With Convergence Guarantees","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Automatic Control","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"Australian Research Council; State Key Laboratory of Industrial Control Technology; Natural Science Foundation of Zhejiang Province; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Convergence (economics); Observer (physics); Simultaneous localization and mapping; Inertial frame of reference; Computer science; Control theory (sociology); Computer vision; Artificial intelligence; Robot; Mobile robot; Control (management); Physics","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":[0.002166318,0.001297511,0.001243317,0.000404219,0.0005164369,0.001231745,0.001856207,0.001735775,0.003245433],"category_scores_gemma":[0.007372882,0.0009228535,0.0006786117,0.0005096362,0.001047199,0.002088635,0.002505497,0.002107075,0.001445649],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007152764,"about_ca_system_score_gemma":0.001790219,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002694912,"about_ca_topic_score_gemma":0.002829797,"domain_scores_codex":[0.9986429,0.0004281402,0.00007803276,0.0003239845,0.0004053608,0.0001216964],"domain_scores_gemma":[0.9983644,0.0006393489,0.0002374429,0.0002801793,0.0004077455,0.00007088542],"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":[0.0001580219,0.00003724054,0.0004325292,0.0002480359,0.00004637172,0.00005696267,0.0001249355,0.8591075,0.00466293,0.03538049,0.003306214,0.09643879],"study_design_scores_gemma":[0.00001647834,0.00004398396,0.00006163108,0.00001264448,0.000004273465,0.00001507838,0.00001021749,0.9867235,0.0008050974,0.01050147,0.001795524,0.00001016824],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0005607621,0.00003972609,0.9988206,0.00003359848,0.0000128984,0.00001031632,0.00001514077,0.0001724315,0.000334477],"genre_scores_gemma":[0.4894488,0.0004186806,0.5039065,0.00023073,0.0001478035,0.0005906897,0.0004169759,0.0005047827,0.004335068],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003245433,"threshold_uncertainty_score":0.01145673,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01447592888181928,"score_gpt":0.2321948693009656,"score_spread":0.2177189404191463,"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."}}