{"id":"W2797183497","doi":"10.5220/0007524800002108","title":"FDMO: Feature Assisted Direct Monocular Odometry","year":2019,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Visual odometry; Artificial intelligence; Feature (linguistics); Computer science; Monocular; Computer vision; Odometry; Direct methods; Heuristic; Pixel; Orb (optics); Pattern recognition (psychology); Image (mathematics); Robot; Mobile robot","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":[0.00004245089,0.00009157652,0.0001155089,0.00006913532,0.00001630133,0.0000313915,0.00005838734,0.0000902566,0.0002864515],"category_scores_gemma":[0.000009719673,0.00008028004,0.00004641341,0.0002214775,0.000004473603,0.00005021295,0.000007909784,0.00007929024,0.0003029894],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003016355,"about_ca_system_score_gemma":0.000003852044,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006954072,"about_ca_topic_score_gemma":0.000002491426,"domain_scores_codex":[0.9995707,0.000009624238,0.00007855915,0.0001057885,0.0001028629,0.0001325213],"domain_scores_gemma":[0.9997091,0.00002234926,0.000008447109,0.0001934204,0.0000226105,0.00004410561],"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":[0.000005066347,0.00002353735,0.00466317,0.00009526372,0.00008304077,0.00000841542,0.00003549482,0.9437938,0.02533699,0.001168049,0.01991333,0.004873876],"study_design_scores_gemma":[0.0003174533,0.00002328499,0.01014978,0.00001857166,0.00001417682,0.000003211255,0.00002333761,0.9444216,0.012341,0.00002558608,0.03242248,0.0002394442],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.3239988,0.001040072,0.1626859,0.0002992334,0.001756898,0.0004666623,0.000009427624,0.001619494,0.5081235],"genre_scores_gemma":[0.9896895,0.00002532362,0.003843895,0.00007209418,0.00004131925,0.000001583331,0.00002604303,0.00002958458,0.006270651],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6656907,"threshold_uncertainty_score":0.3894417,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005097100519059049,"score_gpt":0.180715156579249,"score_spread":0.17561805606019,"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."}}