{"id":"W4313316246","doi":"10.1109/lra.2022.3233232","title":"Safe and Smooth: Certified Continuous-Time Range-Only Localization","year":2022,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung","keywords":"Solver; Certificate; Mathematical optimization; Range (aeronautics); Smoothness; Computer science; Mathematics; Algorithm","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":[0.0001004474,0.0001214803,0.0001429945,0.0001375537,0.0002263155,0.00006881382,0.00007052976,0.00004916112,0.00005299346],"category_scores_gemma":[0.00001271542,0.0001332511,0.00002180432,0.0001692448,0.00005341595,0.000111532,0.00003126714,0.0001077118,0.000009629356],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004834294,"about_ca_system_score_gemma":0.000006445066,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006071714,"about_ca_topic_score_gemma":0.000001391715,"domain_scores_codex":[0.999323,0.0000284015,0.0002030584,0.0001409093,0.0001480045,0.0001566359],"domain_scores_gemma":[0.9997607,0.0000335714,0.00004260298,0.0001120349,0.00001902297,0.00003205114],"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":[0.000004692702,0.00001013897,0.001189744,0.00005325739,0.0000228829,0.000005554245,0.0003164154,0.9710889,0.00909749,0.001187352,0.008069081,0.008954519],"study_design_scores_gemma":[0.0004480751,0.00002603102,0.001485477,0.00001153273,0.00002208652,0.00001958715,0.0001037138,0.9892068,0.001630717,0.0001667393,0.006650159,0.0002291049],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3147898,0.0002930454,0.6785486,0.003301751,0.000678225,0.0003422736,0.00002625789,0.001502472,0.0005175364],"genre_scores_gemma":[0.9973319,0.00005186842,0.001515206,0.0008963078,0.00003758966,0.00001774699,0.00003739808,0.000029275,0.00008276269],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.682542,"threshold_uncertainty_score":0.5433824,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006363341950708757,"score_gpt":0.1844828080831168,"score_spread":0.1781194661324081,"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."}}