{"id":"W2080602205","doi":"10.1017/s0263574709990312","title":"Landmark detection and localization for mobile robot applications: a multisensor approach","year":2009,"lang":"en","type":"article","venue":"Robotica","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada; Memorial University of Newfoundland; Else Kröner-Fresenius-Stiftung","keywords":"Computer vision; Landmark; Artificial intelligence; Computer science; Extended Kalman filter; Sensor fusion; Kalman filter; Simultaneous localization and mapping; Mobile robot; Laser scanning; Robot; Laser","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":[0.0005768291,0.000490547,0.0005460316,0.001364604,0.0003086925,0.0008608141,0.0005948123,0.0006932674,0.001642231],"category_scores_gemma":[0.001080591,0.0003588168,0.0005819108,0.000805281,0.0003977517,0.001507055,0.0008899032,0.0004775608,0.0007040506],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002996206,"about_ca_system_score_gemma":0.0003178465,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001236766,"about_ca_topic_score_gemma":0.001274953,"domain_scores_codex":[0.9994628,0.0001249358,0.00003518932,0.0001061762,0.0002387644,0.00003220057],"domain_scores_gemma":[0.9996471,0.00009565342,0.00004684268,0.00005540385,0.0001373128,0.00001771896],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001890608,0.00008346023,0.001690199,0.0004101537,0.0001365045,0.0004251755,0.0002177837,0.1210614,0.08053559,0.0177374,0.003565177,0.773948],"study_design_scores_gemma":[0.0000215504,0.0002194542,0.002499395,0.00006797253,0.00008101427,0.0004402482,0.0001612766,0.9286421,0.03534799,0.01766969,0.01477077,0.00007846843],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005850737,0.0007861491,0.9917762,0.000140522,0.00005585421,0.00001675793,0.00002404955,0.0005221368,0.0008275602],"genre_scores_gemma":[0.3903992,0.001681217,0.6032044,0.0001424678,0.0001721438,0.0001022575,0.0001406404,0.0001042576,0.004053541],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001642231,"threshold_uncertainty_score":0.00549382,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007296609535271638,"score_gpt":0.2126161546655106,"score_spread":0.205319545130239,"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."}}