{"id":"W4309740957","doi":"10.3390/s22228967","title":"A Generic Image Processing Pipeline for Enhancing Accuracy and Robustness of Visual Odometry","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Robustness (evolution); Computer science; Computer vision; Outlier; Feature extraction; Pipeline (software); Visual odometry; Monocular; Odometry; Feature (linguistics); Histogram; Pattern recognition (psychology); Mobile robot; Robot; Image (mathematics)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001357569,0.00009841063,0.000156714,0.0001305804,0.0001114448,0.00002077087,0.00004885952,0.00002717293,0.00002146437],"category_scores_gemma":[0.00009440062,0.0001076577,0.00003315751,0.0002620169,0.00002053632,0.00005674541,0.00002770915,0.00007659149,3.960309e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003387453,"about_ca_system_score_gemma":0.00001633597,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004190301,"about_ca_topic_score_gemma":0.000002638589,"domain_scores_codex":[0.9993441,0.00001978572,0.000220033,0.0001315519,0.0001242222,0.0001602751],"domain_scores_gemma":[0.9996719,0.00008356045,0.00005269978,0.00007782599,0.00007753113,0.00003648284],"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":[0.00001284933,0.00001770299,0.000018284,0.0002563591,0.000007229126,0.00000159379,0.0001516077,0.9494203,0.04254258,0.0000134926,0.0001173763,0.007440634],"study_design_scores_gemma":[0.000296367,0.00003544927,0.00003062842,0.00001317135,0.00001806507,0.00000793345,0.0005023872,0.9775849,0.0209919,0.00001190425,0.0003833678,0.0001239267],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5978217,0.0002473731,0.4015462,0.00002296714,0.0001057089,0.0001341099,0.00001095886,0.00006256792,0.00004834026],"genre_scores_gemma":[0.9901914,0.00002562005,0.009540962,0.00001687158,0.00008585484,0.00001363979,0.00002853544,0.00004076371,0.00005636883],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3923697,"threshold_uncertainty_score":0.4390155,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01151528692475873,"score_gpt":0.247688176695216,"score_spread":0.2361728897704573,"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."}}