{"id":"W4379409302","doi":"10.1109/tmech.2023.3276335","title":"Robust Visual Odometry On $\\text{SE}(3)$: Design and Verification","year":2023,"lang":"en","type":"article","venue":"IEEE/ASME Transactions on Mechatronics","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Visual odometry; Odometry; Computer science; Artificial intelligence; Computer vision; Trajectory; Feature (linguistics); Noise (video); Pose; Robot; Image (mathematics); Mobile robot","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":[0.0007645685,0.0005214697,0.0003943171,0.0003111046,0.0002400166,0.0007231685,0.001186022,0.0005992473,0.001945829],"category_scores_gemma":[0.002115944,0.0002701184,0.0003665749,0.0001920872,0.0006144942,0.0006328859,0.0009973543,0.0004529022,0.0006560742],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004808309,"about_ca_system_score_gemma":0.001168871,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003158611,"about_ca_topic_score_gemma":0.002306166,"domain_scores_codex":[0.999326,0.00008259016,0.00002961589,0.0001578545,0.0003358906,0.00006796273],"domain_scores_gemma":[0.9992157,0.0001624748,0.000133195,0.0001363033,0.0003262489,0.00002608186],"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":[0.0002816223,0.00011147,0.002200027,0.0003565117,0.00006255201,0.0001987635,0.0001642003,0.545031,0.1009323,0.01771346,0.00264675,0.3303014],"study_design_scores_gemma":[0.00002623138,0.0002492764,0.0007702954,0.00002443671,0.00001136869,0.00007204041,0.00002489512,0.9620806,0.03102338,0.002082794,0.003616709,0.00001796758],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009364076,0.00003916539,0.9888414,0.00004428381,0.00001809939,0.0000694636,0.00004147569,0.0005073483,0.001074744],"genre_scores_gemma":[0.7060001,0.00009878998,0.2918501,0.00006989922,0.000015313,0.0002487508,0.0001631413,0.00006186141,0.001492145],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003158611,"threshold_uncertainty_score":0.006509423,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04195863682000009,"score_gpt":0.2455336927059454,"score_spread":0.2035750558859453,"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."}}