{"id":"W4389892745","doi":"10.32920/24625119","title":"Aided Visual Odometry: A Sparse Bundle Adjustment Solution for Rover Navigation","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Bundle adjustment; Visual odometry; Computer vision; Artificial intelligence; Computer science; Odometry; Trajectory; Simultaneous localization and mapping; Structure from motion; RANSAC; Motion (physics); Mobile robot; Robot; Image (mathematics)","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":[0.0003376181,0.0007767225,0.0006590444,0.0005541954,0.0004572453,0.0008543602,0.00103971,0.001067394,0.001915019],"category_scores_gemma":[0.001691679,0.0005641032,0.0005248702,0.001058846,0.0003562749,0.001096707,0.00127816,0.001252841,0.001429551],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002818094,"about_ca_system_score_gemma":0.0009957493,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004801755,"about_ca_topic_score_gemma":0.007468819,"domain_scores_codex":[0.999632,0.0000671322,0.00001318082,0.00009873659,0.0001506162,0.00003825669],"domain_scores_gemma":[0.9997078,0.00006165067,0.00005103621,0.00006657951,0.00009774942,0.00001525872],"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":[0.0001020463,0.00008306638,0.001225482,0.0001340553,0.0001105644,0.0000865265,0.0002348954,0.4225711,0.0226007,0.01323473,0.01059493,0.5290218],"study_design_scores_gemma":[0.00002011107,0.00003878256,0.0005042539,0.00001030131,0.000008645722,0.00005060544,0.00003496372,0.9818668,0.003635278,0.006434763,0.007380477,0.00001497083],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00375746,0.00006267556,0.9950446,0.00006627697,0.00002734084,0.00002024144,0.00008181055,0.0004218685,0.0005177768],"genre_scores_gemma":[0.1315074,0.0002385508,0.8631438,0.00007552181,0.00008251677,0.0001422385,0.0007696241,0.0002111271,0.003829191],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004801755,"threshold_uncertainty_score":0.009547591,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06523404297857842,"score_gpt":0.3600603335871604,"score_spread":0.294826290608582,"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."}}