{"id":"W4317553727","doi":"10.1109/lra.2023.3238173","title":"An Efficient Global Optimality Certificate for Landmark-Based SLAM","year":2023,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Landmark; Computer science; Simultaneous localization and mapping; Solver; Mathematical optimization; Graph; Certificate; Robustness (evolution); Theoretical computer science; Artificial intelligence; Mathematics; Robot; Mobile robot","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":[0.003314859,0.001046096,0.001795944,0.001351194,0.0009857776,0.002081303,0.002137784,0.001408146,0.005276837],"category_scores_gemma":[0.01595777,0.0007167253,0.001267202,0.001492498,0.002227456,0.003343758,0.004625709,0.003952557,0.001900579],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00150872,"about_ca_system_score_gemma":0.003591529,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003013157,"about_ca_topic_score_gemma":0.002903688,"domain_scores_codex":[0.9972319,0.0007434309,0.0001686914,0.0003998264,0.001110543,0.0003456165],"domain_scores_gemma":[0.9940896,0.002985954,0.0004549643,0.001244377,0.0009828241,0.0002422326],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002636239,0.00007205611,0.0006407682,0.0001669743,0.00003378194,0.0001387697,0.0001677603,0.6914493,0.006656,0.2068867,0.005982308,0.08754197],"study_design_scores_gemma":[0.00002273099,0.00004713287,0.00009724958,0.00001524268,0.000005195101,0.00002609954,0.00002414246,0.9270148,0.002693541,0.06856459,0.001475036,0.00001423721],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005358896,0.00005654424,0.9918035,0.0001084223,0.00003098339,0.00004850287,0.00006724279,0.0006136465,0.001912112],"genre_scores_gemma":[0.4138591,0.000333219,0.5799056,0.0001724785,0.000137271,0.0003145456,0.0007509678,0.0006839691,0.00384286],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005276837,"threshold_uncertainty_score":0.01765275,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03365948370194767,"score_gpt":0.2627700697857473,"score_spread":0.2291105860837996,"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."}}