{"id":"W3007772440","doi":"10.1007/978-3-030-41590-7_20","title":"Robustifying Direct VO to Large Baseline Motions","year":2020,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Initialization; Computer science; Artificial intelligence; Odometry; Process (computing); Frame (networking); Heuristic; Baseline (sea); Feature (linguistics); Motion (physics); Computer vision; 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":[0.0003933222,0.001224825,0.0009006634,0.0005775316,0.0003419784,0.001120375,0.0008676553,0.001025575,0.004934577],"category_scores_gemma":[0.002493822,0.0006286288,0.000761856,0.0005363034,0.0007486708,0.001204928,0.00294654,0.001091224,0.002583019],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003274016,"about_ca_system_score_gemma":0.000386172,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003192652,"about_ca_topic_score_gemma":0.004547365,"domain_scores_codex":[0.9995973,0.00004414104,0.00001422876,0.000146834,0.0001362171,0.00006136577],"domain_scores_gemma":[0.9994936,0.0001561913,0.000054053,0.0001726413,0.00009852333,0.00002496192],"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":[0.0003659776,0.00005485554,0.0006914746,0.000243088,0.00008893834,0.0002456943,0.0002507107,0.2007508,0.102934,0.01173021,0.005194837,0.6774493],"study_design_scores_gemma":[0.00002083121,0.0001647491,0.001404159,0.00004769357,0.00002220632,0.0004891853,0.00012234,0.9337237,0.03016275,0.01860865,0.01520386,0.00002978912],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01266074,0.0004185888,0.980884,0.00006383365,0.0001224955,0.00003275469,0.00008589445,0.0008906624,0.004841028],"genre_scores_gemma":[0.5234987,0.001166354,0.4225208,0.00029781,0.0002724091,0.0001410864,0.001071133,0.001102353,0.04992939],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004934577,"threshold_uncertainty_score":0.0165078,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03664899484932066,"score_gpt":0.2584013098097654,"score_spread":0.2217523149604447,"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."}}