{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001170377,0.0001842928,0.0002708414,0.0001010984,0.0001016358,0.0001436031,0.00004128588,0.000119415,0.000001257255],"category_scores_gemma":[0.00001095791,0.0001930991,0.00001712112,0.00019591,0.00003094458,0.0000473635,0.00001603954,0.00009998839,0.000002667317],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006156551,"about_ca_system_score_gemma":0.00001272893,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002700597,"about_ca_topic_score_gemma":0.00003594049,"domain_scores_codex":[0.9989678,0.00003100738,0.0003952601,0.0002033251,0.00009496212,0.000307698],"domain_scores_gemma":[0.9996392,0.00008801885,0.00005202879,0.0001190791,0.00003697716,0.00006475166],"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":[0.000001161309,0.000009163537,0.0134876,0.0001444968,0.00001046463,0.00004623637,0.0001580504,0.9830033,0.00006078489,0.002654548,0.0002506707,0.0001735892],"study_design_scores_gemma":[0.0002588156,0.0000152972,0.0009049982,0.0001615764,0.000009375096,0.00003675052,0.0001235333,0.9943189,0.00000683507,0.0001055026,0.003825348,0.0002330786],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07255004,0.006761249,0.9179624,0.0000626734,0.0006079929,0.0004467519,0.00000819824,0.0002914843,0.001309149],"genre_scores_gemma":[0.9979653,0.00009490483,0.001404193,0.00002877237,0.0002343422,0.00000576256,0.00003173223,0.0000422664,0.0001927724],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9254152,"threshold_uncertainty_score":0.7874353,"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."}}