{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005351622,0.0008783809,0.0006006446,0.001131076,0.0003650185,0.0007119244,0.001092061,0.0006592017,0.00286765],"category_scores_gemma":[0.001644763,0.0004491268,0.0006114105,0.0009340086,0.0004288878,0.001007383,0.001238016,0.0006318026,0.001617302],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004098803,"about_ca_system_score_gemma":0.0007500552,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002552917,"about_ca_topic_score_gemma":0.002330207,"domain_scores_codex":[0.9994578,0.00003893182,0.00002665585,0.000162197,0.0002410914,0.00007328188],"domain_scores_gemma":[0.9995437,0.00007186546,0.00005684331,0.0001127234,0.0001963824,0.00001845925],"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":[0.0002176967,0.00007105194,0.001461145,0.0002374271,0.00005860598,0.0001020993,0.0001206824,0.03026703,0.26301,0.004532716,0.002975285,0.6969463],"study_design_scores_gemma":[0.00005386762,0.0004163081,0.007284335,0.00004773859,0.00006517131,0.0006533476,0.00009316637,0.66699,0.2874615,0.004828333,0.03200375,0.0001023821],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005859341,0.00007373582,0.9908671,0.00002401019,0.00002019419,0.00005145886,0.00007311659,0.002351366,0.0006796374],"genre_scores_gemma":[0.1204397,0.0001679768,0.8771013,0.00005379948,0.00002816896,0.0001059611,0.0004495675,0.0001711418,0.001482334],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00286765,"threshold_uncertainty_score":0.009593189,"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."}}