{"id":"W1959692127","doi":"10.1007/s10514-015-9512-6","title":"A robust loop-closure method for visual SLAM in unstructured seafloor environments","year":2015,"lang":"en","type":"article","venue":"Autonomous Robots","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Yildiz Teknik Üniversitesi; Korea Advanced Institute of Science and Technology; Ministry of Oceans and Fisheries","keywords":"Computer science; Simultaneous localization and mapping; Monocular; Artificial intelligence; Context (archaeology); Computer vision; Matching (statistics); Underwater; Loop (graph theory); Closure (psychology); Robot; Mobile robot; Geology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002190057,0.0002276312,0.0002801311,0.000129147,0.00003701471,0.00004026047,0.0001322785,0.0001835437,0.00002432083],"category_scores_gemma":[0.00003689657,0.0002395456,0.00006777818,0.0001387141,0.00001991775,0.000108396,0.00002449039,0.0001360997,0.00003497738],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002848624,"about_ca_system_score_gemma":0.00004960143,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004919984,"about_ca_topic_score_gemma":0.00005046822,"domain_scores_codex":[0.9988233,0.00004425796,0.0003216948,0.0002771405,0.0001643909,0.0003692198],"domain_scores_gemma":[0.9995248,0.00004614896,0.00004342781,0.0002130598,0.00002386134,0.000148691],"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":[0.00001649747,0.00003415865,0.000413127,0.00002924973,0.00002919425,0.00000801217,0.0002342661,0.9925669,0.001213797,0.000379452,0.0005314564,0.004543848],"study_design_scores_gemma":[0.001344627,0.00008350288,0.0009517656,0.00001521315,0.00002245648,0.000007911068,0.00005336869,0.9887247,0.003893435,0.0005291398,0.004083162,0.000290724],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0195912,0.0001945433,0.9780334,0.00007755262,0.0006392688,0.0004934348,0.00001397936,0.0001614503,0.0007952339],"genre_scores_gemma":[0.6856909,0.00001850486,0.312313,0.0001824579,0.0002513196,0.00009621512,0.0002165022,0.0001637182,0.001067401],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6660997,"threshold_uncertainty_score":0.9768388,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02726451118395097,"score_gpt":0.253098188650703,"score_spread":0.225833677466752,"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."}}