{"id":"W3199004518","doi":"10.3390/rs13183591","title":"Point-Line Visual Stereo SLAM Using EDlines and PL-BoW","year":2021,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Chongqing Municipal Education Commission; National Natural Science Foundation of China","keywords":"Artificial intelligence; Simultaneous localization and mapping; Visual odometry; Computer vision; Computer science; Robustness (evolution); Outlier; Line (geometry); Point (geometry); Robot; Mathematics; Mobile 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007082005,0.0001411335,0.0001777719,0.00006655595,0.00007735834,0.00008287062,0.00001923845,0.00008133621,0.000005868197],"category_scores_gemma":[0.00005093977,0.0001546775,0.00003699791,0.0001810904,0.00002053735,0.00007422702,0.00002590008,0.0001063316,0.000004533062],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000041815,"about_ca_system_score_gemma":0.00002011319,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002539314,"about_ca_topic_score_gemma":0.00002533752,"domain_scores_codex":[0.9992652,0.00003051064,0.0002055779,0.0001771173,0.0001139501,0.0002076276],"domain_scores_gemma":[0.9996334,0.00004014417,0.00002492232,0.0001389534,0.00009520011,0.00006735213],"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":[0.000005829359,0.000006834312,0.00002753651,0.000121307,0.00003759384,0.000170299,0.0001755302,0.6686866,0.2396457,0.00004617394,0.00004302242,0.09103356],"study_design_scores_gemma":[0.0002127406,0.000009902234,0.0000384203,0.0001267102,0.00002509028,0.0001654658,0.00008986759,0.9626662,0.03605907,0.0001236222,0.0003101907,0.0001727854],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6055628,0.0003108473,0.3933352,0.00007776961,0.0002809841,0.00003940547,9.782486e-7,0.0001074512,0.0002845046],"genre_scores_gemma":[0.9415471,0.0001170943,0.05770008,0.0001235683,0.0003399835,1.812727e-9,0.00002078434,0.00005472976,0.00009668162],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3359843,"threshold_uncertainty_score":0.6307566,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01822982466865924,"score_gpt":0.2497366803412196,"score_spread":0.2315068556725604,"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."}}