{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007674276,0.0001321962,0.0001435854,0.00007850788,0.000118483,0.00005165529,0.00004904582,0.0001103395,0.000003142445],"category_scores_gemma":[0.00001863374,0.0001360088,0.00003527855,0.0001792135,0.00002001237,0.00007555164,0.000004639217,0.0000651894,0.000005663674],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003996892,"about_ca_system_score_gemma":0.000005360282,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002965523,"about_ca_topic_score_gemma":0.000003495295,"domain_scores_codex":[0.9993221,0.00001477561,0.0001927971,0.000200344,0.00008704617,0.0001829312],"domain_scores_gemma":[0.999631,0.0000444385,0.00002588488,0.0001609641,0.00006475521,0.00007290216],"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":[0.000009311068,0.00003833356,0.00003998352,0.0000710788,0.000009574779,1.674986e-7,0.00004925159,0.9658364,0.003645127,0.000977692,0.00005498341,0.02926809],"study_design_scores_gemma":[0.0003852527,0.00007820764,0.0004543921,0.00001012883,0.00002972877,0.000005559006,0.00003191972,0.994128,0.001745988,0.0004602028,0.002502862,0.0001677538],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0007269644,0.0003061226,0.9971959,0.00002505476,0.00005546564,0.000923661,0.000005049159,0.0002708272,0.0004909687],"genre_scores_gemma":[0.9605649,0.0001185454,0.03876508,0.00007350543,0.0001026355,0.000221795,0.00007438895,0.00002988911,0.00004932664],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9598379,"threshold_uncertainty_score":0.554628,"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."}}