{"id":"W1996386660","doi":"10.1109/ccece.2006.277572","title":"A Visual SLAM Solution Based on High Level Geometry Knowledge and Kalman Filtering","year":2006,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Initialization; Extended Kalman filter; Robustness (evolution); Simultaneous localization and mapping; Kalman filter; Computer vision; Artificial intelligence; Geometric primitive; Computer science; Feature (linguistics); Epipolar geometry; Noise (video); Robot; Image (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":[0.00005800769,0.0001163009,0.00009807735,0.0001522671,0.00005793127,0.00003440466,0.00003143174,0.0000681836,0.00005268059],"category_scores_gemma":[0.000007205059,0.0001148975,0.00002279086,0.0001230207,0.00001340304,0.00005170163,0.00001000185,0.0000624602,0.00002683787],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005078631,"about_ca_system_score_gemma":0.000006180957,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001186626,"about_ca_topic_score_gemma":0.00008059599,"domain_scores_codex":[0.9994666,0.00001071667,0.0001370156,0.0001311883,0.00008417851,0.0001702913],"domain_scores_gemma":[0.9998005,0.00003464105,0.00001132532,0.0000921083,0.00002375364,0.00003765861],"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":[0.00000717427,0.00006223947,0.0006298656,0.00007701202,0.000007065115,0.000003109794,0.00001578121,0.9729947,0.0163064,0.003439069,0.002153139,0.004304466],"study_design_scores_gemma":[0.0003398377,0.00004938509,0.01578631,0.00002395486,0.000006332698,9.266455e-7,0.000003054711,0.9710854,0.01173304,0.00006686408,0.0007533074,0.0001516038],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.291378,0.00006093106,0.7003509,0.00003994452,0.0002032392,0.00008302336,0.000005525194,0.0002543784,0.007623978],"genre_scores_gemma":[0.9962929,0.000004969286,0.003008615,0.00003353004,0.0001157065,0.000003622707,0.00005032222,0.00002632627,0.0004640316],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7049149,"threshold_uncertainty_score":0.4685383,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01320920572937963,"score_gpt":0.2190952988452704,"score_spread":0.2058860931158908,"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."}}