{"id":"W2964235957","doi":"10.1109/3dv.2017.00027","title":"GSLAM: Initialization-Robust Monocular Visual SLAM via Global Structure-from-Motion","year":2017,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Initialization; Artificial intelligence; Visual odometry; Simultaneous localization and mapping; Computer vision; Computer science; Monocular; Robustness (evolution); Structure from motion; Ground truth; Motion (physics); Robot; Mobile robot","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":[0.0008130717,0.001659384,0.00157885,0.001488201,0.0006415099,0.001161165,0.002117895,0.001033649,0.003351965],"category_scores_gemma":[0.002017189,0.0006960976,0.0008506342,0.002247575,0.0006830221,0.001492553,0.003039599,0.001367045,0.00276755],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000638403,"about_ca_system_score_gemma":0.001791874,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008536724,"about_ca_topic_score_gemma":0.01555165,"domain_scores_codex":[0.9988517,0.0001726318,0.00004206194,0.0003314118,0.0004713965,0.0001308967],"domain_scores_gemma":[0.99932,0.00008311764,0.00006630206,0.0003040108,0.000182224,0.00004420926],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002254823,0.0001034673,0.0007605479,0.000276936,0.0001385785,0.0001173962,0.0001384929,0.09138834,0.0258098,0.004312944,0.02692665,0.8498014],"study_design_scores_gemma":[0.00008015164,0.0001555271,0.00124899,0.00004171928,0.00002245355,0.0001737861,0.00008517,0.9523283,0.02228564,0.007620899,0.01590374,0.00005357944],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007356931,0.0003241674,0.9786429,0.00007205187,0.00009415735,0.0000835916,0.0006224121,0.01131681,0.00148697],"genre_scores_gemma":[0.1957313,0.0003487219,0.7937586,0.0002136544,0.00008713974,0.000243904,0.004378142,0.001035548,0.004202982],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008536724,"threshold_uncertainty_score":0.01697409,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01374085816013249,"score_gpt":0.2432400375916696,"score_spread":0.2294991794315371,"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."}}