{"id":"W2904369717","doi":"10.1109/crv.2018.00057","title":"Indoor Localization in Dynamic Human Environments Using Visual Odometry and Global Pose Refinement","year":2018,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Visual odometry; Odometry; Computer science; Artificial intelligence; Computer vision; Robot; Mobile robot; Pose; Task (project management); Key (lock); Engineering","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0005779188,0.0008611751,0.0006192526,0.0006597621,0.0003208527,0.0005852864,0.0009184403,0.0004191839,0.0008320672],"category_scores_gemma":[0.001681309,0.0004794908,0.0004888852,0.000698099,0.0008690465,0.001230129,0.00145257,0.0004711703,0.0005575593],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003086698,"about_ca_system_score_gemma":0.000647496,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007188273,"about_ca_topic_score_gemma":0.01145388,"domain_scores_codex":[0.9994962,0.0001194084,0.00001832346,0.0001311592,0.0001829253,0.00005194018],"domain_scores_gemma":[0.9994881,0.0001156217,0.00009348808,0.0001841978,0.00009324527,0.00002520872],"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":[0.0002150679,0.000118455,0.003307489,0.0001691128,0.0001254372,0.0001509524,0.0003715121,0.4388187,0.07702328,0.006904084,0.001097326,0.4716986],"study_design_scores_gemma":[0.00002696489,0.0001902605,0.00331183,0.00002570364,0.00003743019,0.0002135224,0.0001615035,0.9586142,0.02660804,0.007310282,0.00345418,0.00004611744],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01699257,0.0001078347,0.9813728,0.00003513802,0.0000165918,0.00001938854,0.00001633344,0.0006489444,0.0007902727],"genre_scores_gemma":[0.5566673,0.000315958,0.4411905,0.0000431603,0.0000303332,0.00004110764,0.0001475028,0.0001251528,0.001439042],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007188273,"threshold_uncertainty_score":0.01429284,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009246656113701016,"score_gpt":0.2642583432046861,"score_spread":0.255011687090985,"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."}}