{"id":"W2171885964","doi":"10.1016/j.robot.2006.05.009","title":"Simultaneous planning, localization, and mapping in a camera sensor network","year":2006,"lang":"en","type":"article","venue":"Robotics and Autonomous Systems","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":75,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Computer vision; Artificial intelligence; Robot; Simultaneous localization and mapping; Context (archaeology); Camera auto-calibration; Kalman filter; Extended Kalman filter; Camera resectioning; Heuristic; Calibration; Exploit; Bundle adjustment; Smart camera; Fiducial marker; Mobile robot; Image (mathematics); Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0006254149,0.0007208405,0.0009143467,0.0007235893,0.0007367448,0.001131618,0.001212872,0.0009560055,0.001000376],"category_scores_gemma":[0.002338645,0.0009627009,0.0004697097,0.001475985,0.0009728274,0.002700933,0.001254056,0.0008622055,0.0002186003],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007301292,"about_ca_system_score_gemma":0.001594087,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01197598,"about_ca_topic_score_gemma":0.01400101,"domain_scores_codex":[0.9992587,0.000161985,0.00003437006,0.0002525776,0.0002173618,0.00007489026],"domain_scores_gemma":[0.9992744,0.0003767566,0.0001209984,0.00008648723,0.00008848082,0.00005277856],"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":[0.0003305479,0.00006012819,0.001205482,0.0001164404,0.00006040551,0.0002621635,0.0002033746,0.8345165,0.009534803,0.0159687,0.001096114,0.1366454],"study_design_scores_gemma":[0.00001844356,0.00004957765,0.0003541532,0.000005447603,0.00001636379,0.0000578186,0.0000359773,0.9883382,0.002657188,0.007468005,0.0009864288,0.00001244317],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02959831,0.0005377949,0.9679742,0.000146292,0.00003616462,0.00003248918,0.00005011942,0.0004000221,0.001224561],"genre_scores_gemma":[0.6867629,0.0009988896,0.3078692,0.0000574785,0.00006977174,0.0001727404,0.0001919546,0.00007871071,0.003798378],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01197598,"threshold_uncertainty_score":0.02381253,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007488634762951442,"score_gpt":0.1914065168926205,"score_spread":0.1839178821296691,"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."}}