{"id":"W2168791921","doi":"10.1109/crv.2007.14","title":"Camera Sensor Model for Visual SLAM","year":2007,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Artificial intelligence; Computer vision; Camera auto-calibration; Computer science; Noise (video); Camera matrix; Camera resectioning; Range (aeronautics); Covariance matrix; Image sensor; Covariance; Gaussian; Pinhole camera model; Calibration; Essential matrix; Gaussian noise; Mathematics; Algorithm; Image (mathematics); Eigenvalues and eigenvectors; Statistics; Engineering","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":[0.0003631451,0.0006910112,0.0006944144,0.0005024167,0.0002487198,0.0007575773,0.001198565,0.0009020753,0.002733507],"category_scores_gemma":[0.001676588,0.0003465702,0.0007257423,0.0006907301,0.0004547425,0.001440795,0.0007779989,0.001007505,0.001434369],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004128599,"about_ca_system_score_gemma":0.0006460518,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00248218,"about_ca_topic_score_gemma":0.002373209,"domain_scores_codex":[0.9992923,0.000122642,0.00002494709,0.0001521042,0.0003636264,0.00004456087],"domain_scores_gemma":[0.9996773,0.00006928802,0.00004686134,0.00007697255,0.0001124527,0.00001715632],"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":[0.0001158411,0.00005583152,0.0006022961,0.0002386491,0.0000879623,0.0002450087,0.0001101072,0.7258092,0.01992458,0.1003784,0.005914206,0.146518],"study_design_scores_gemma":[0.000009260208,0.00003768429,0.0001634984,0.00001377829,0.000009751484,0.00007686319,0.00001291433,0.9727137,0.002242733,0.01834325,0.006363085,0.00001348246],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001060442,0.0001074779,0.9970631,0.00004570262,0.00004727116,0.00001069633,0.00005692862,0.0003033601,0.001305043],"genre_scores_gemma":[0.5660536,0.000943958,0.4206151,0.0003915283,0.0001485705,0.0002977281,0.0009468883,0.0003902556,0.01021234],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002733507,"threshold_uncertainty_score":0.009144485,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01443137707593176,"score_gpt":0.2521852091025996,"score_spread":0.2377538320266678,"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."}}