{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003646398,0.0007991102,0.0008023848,0.00114557,0.0004042405,0.0006751814,0.001197974,0.0005835824,0.003372686],"category_scores_gemma":[0.001025533,0.0004154146,0.0004934787,0.0009165962,0.0003005688,0.0007116229,0.002379338,0.0005623303,0.001411188],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002965579,"about_ca_system_score_gemma":0.0007167909,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004745001,"about_ca_topic_score_gemma":0.00789198,"domain_scores_codex":[0.9996123,0.00002854529,0.00001270847,0.0001140805,0.00018102,0.00005135522],"domain_scores_gemma":[0.9997039,0.000036074,0.00003902833,0.0001119959,0.00008698607,0.00002199638],"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":[0.0003019419,0.00012536,0.00567831,0.0003892531,0.0002064799,0.0001394071,0.0001893771,0.01943507,0.0658997,0.003526038,0.01860893,0.8855002],"study_design_scores_gemma":[0.0002093918,0.0006611925,0.0284481,0.0001257213,0.0001007393,0.001531624,0.0001904733,0.7657777,0.08718071,0.008129049,0.107425,0.0002202324],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04319607,0.0006714361,0.9282904,0.0001277094,0.0002622314,0.0002440998,0.002054889,0.01712053,0.00803277],"genre_scores_gemma":[0.4271019,0.0003061347,0.5615557,0.0001819218,0.00008275796,0.0002628197,0.003655107,0.0003761695,0.006477498],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004745001,"threshold_uncertainty_score":0.0112828,"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."}}