{"id":"W4405603561","doi":"10.2316/j.2025.206-1081","title":"A ROBUST MONOCULAR VISUAL SLAM SYSTEM WITH POINT AND LINE FEATURES, 43-55.","year":2024,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Spatial Cognition and Navigation","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Monocular; Artificial intelligence; Line (geometry); Computer vision; Computer science; Point (geometry); Mathematics; Geometry","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006424791,0.0006371011,0.0009737029,0.0005157258,0.0005909979,0.0007896233,0.0009871043,0.0007117515,0.00471607],"category_scores_gemma":[0.001248862,0.0006062585,0.0003610321,0.0005696538,0.0003278917,0.001173035,0.001178201,0.000568465,0.003077679],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004308854,"about_ca_system_score_gemma":0.001614709,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009408385,"about_ca_topic_score_gemma":0.01623406,"domain_scores_codex":[0.9995839,0.00003910199,0.0000223216,0.0001168148,0.0001815195,0.00005646135],"domain_scores_gemma":[0.999629,0.00002748798,0.00002747459,0.0000927225,0.0001883723,0.00003485281],"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":[0.0009262964,0.0001504803,0.001297694,0.0002909565,0.0001344965,0.0002835531,0.0001323737,0.04214082,0.2211717,0.003377352,0.02766644,0.7024278],"study_design_scores_gemma":[0.000342542,0.0007263014,0.009820675,0.00006836365,0.0001507291,0.0007103507,0.0001661006,0.8486537,0.09816908,0.004527926,0.03653576,0.0001284724],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06107261,0.001220273,0.9142939,0.0003457567,0.0005555609,0.0001915796,0.001530255,0.01350781,0.00728225],"genre_scores_gemma":[0.5317924,0.0002930988,0.4535277,0.0002471719,0.00007812927,0.0001610737,0.002077484,0.000363897,0.01145903],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009408385,"threshold_uncertainty_score":0.01870722,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008674778666835754,"score_gpt":0.2313545065438045,"score_spread":0.2226797278769687,"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."}}