{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004622981,0.001141588,0.0008168179,0.001484674,0.0003642475,0.0007696655,0.001273116,0.0007690038,0.002137883],"category_scores_gemma":[0.001559986,0.0005430019,0.0008037331,0.001707701,0.0004807074,0.001954197,0.001694356,0.001068624,0.001174717],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003919183,"about_ca_system_score_gemma":0.000707644,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00304253,"about_ca_topic_score_gemma":0.003338977,"domain_scores_codex":[0.9990566,0.0001072735,0.00004148316,0.0002813335,0.0004219045,0.00009130669],"domain_scores_gemma":[0.9993259,0.00008090919,0.0001426932,0.0001527235,0.0002545963,0.00004317132],"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":[0.0003767308,0.000121605,0.001442482,0.0002197843,0.0001074406,0.0001349063,0.0002023594,0.06324247,0.06656545,0.004570621,0.00535238,0.8576637],"study_design_scores_gemma":[0.00008477385,0.0003907765,0.002275297,0.00003501527,0.00004476458,0.0004263695,0.0001797149,0.9331781,0.04613434,0.005182327,0.01201208,0.0000565014],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01187453,0.0001821306,0.9849135,0.00004289812,0.00006815922,0.00004385131,0.00009764856,0.001958455,0.0008188597],"genre_scores_gemma":[0.3043541,0.0003287183,0.6896224,0.0001485814,0.00009665704,0.0001708387,0.0008529666,0.0003408523,0.004084973],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00304253,"threshold_uncertainty_score":0.007151902,"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."}}