{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003315399,0.001045307,0.0007250562,0.0004228193,0.000302808,0.0008939723,0.0008969034,0.000656641,0.007420927],"category_scores_gemma":[0.001724601,0.0003850912,0.0003855178,0.0007251108,0.0005133489,0.001253274,0.00191914,0.0006805086,0.001803313],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003440651,"about_ca_system_score_gemma":0.0005279786,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002638489,"about_ca_topic_score_gemma":0.002870752,"domain_scores_codex":[0.9992039,0.0001283647,0.00003056282,0.0002340488,0.0003189068,0.0000842587],"domain_scores_gemma":[0.9995449,0.00006104793,0.00004194111,0.000177142,0.0001460222,0.00002900459],"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":[0.0004602507,0.00009364517,0.001084327,0.0006221374,0.0001300989,0.0002825061,0.0002133952,0.1885968,0.104488,0.02241907,0.02055649,0.6610532],"study_design_scores_gemma":[0.00009695762,0.0003273731,0.002997338,0.0000603612,0.00003443974,0.000426542,0.0001072197,0.908079,0.02884205,0.02662102,0.03233529,0.00007231776],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02104367,0.001016332,0.9615838,0.000214916,0.0004844797,0.00008734852,0.0005701415,0.003082639,0.01191667],"genre_scores_gemma":[0.7291426,0.0008285753,0.2581752,0.000442369,0.0001891966,0.0001845645,0.000977634,0.0002856228,0.009774229],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007420927,"threshold_uncertainty_score":0.02482545,"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."}}