{"id":"W2602414472","doi":"10.15353/vsnl.v2i1.109","title":"Scaled Monocular Visual SLAM","year":2016,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University; University of Waterloo","funders":"","keywords":"Monocular; Focus (optics); Computer vision; Artificial intelligence; Scale (ratio); Computer science; Metric (unit); Simultaneous localization and mapping; Motion (physics); Geography; Robot; Mobile robot; Engineering; Cartography; Optics; Physics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.000209477,0.00008199451,0.0001562293,0.0001400305,0.00004043915,0.00007860614,0.00004477584,0.00002194279,0.000006403512],"category_scores_gemma":[0.00002206323,0.00005236656,0.00004649102,0.00005310374,0.00002191302,0.0002050848,0.000007219055,0.00004924019,0.000006975918],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003558003,"about_ca_system_score_gemma":0.00001577507,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001500788,"about_ca_topic_score_gemma":6.244787e-8,"domain_scores_codex":[0.9992117,0.00003556764,0.0003646792,0.00005148745,0.0002504403,0.00008608185],"domain_scores_gemma":[0.9994791,0.00009954098,0.00009829709,0.00003546901,0.0002119186,0.00007564763],"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":[0.00001538364,0.0000233398,0.002237342,0.00005396012,0.00004250291,0.0000191934,0.0000838972,0.9573662,0.006683855,0.001056286,0.002913014,0.02950508],"study_design_scores_gemma":[0.0006924184,0.00004384392,0.005166478,0.000276378,0.00001026336,0.0001761595,0.00003665679,0.9899253,0.0001316091,0.0005800757,0.002871169,0.0000896341],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.157831,0.0009177683,0.8400977,0.000313255,0.0006068842,0.0000468247,0.000001537714,0.00002774116,0.0001572427],"genre_scores_gemma":[0.9982358,0.00005080508,0.001460898,0.00003127747,0.0001660341,3.459729e-7,0.000001375461,0.00001387829,0.00003958614],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8404047,"threshold_uncertainty_score":0.2135446,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003979125726595468,"score_gpt":0.2273840900549399,"score_spread":0.2234049643283444,"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."}}