{"id":"W2159523073","doi":"10.1109/ical.2008.4636224","title":"Mobile robot localization and object pose estimation using optical encoder, vision and laser sensors","year":2008,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer vision; Artificial intelligence; Mobile robot; Computer science; Workspace; Odometry; Pose; Robot; Omnidirectional camera; Omnidirectional antenna","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":[0.00006143883,0.0001324539,0.0001318703,0.00008723402,0.0001260601,0.00004128325,0.00002095666,0.00009657092,0.00002021272],"category_scores_gemma":[0.00002323087,0.0001224944,0.00001654245,0.0001347545,0.00005486728,0.00019378,0.00001583346,0.00005995198,0.000004190825],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003377829,"about_ca_system_score_gemma":0.00000886741,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001989746,"about_ca_topic_score_gemma":0.000004752791,"domain_scores_codex":[0.9993593,0.00001942034,0.0001862602,0.0001631634,0.0001287454,0.000143165],"domain_scores_gemma":[0.9997197,0.0000362923,0.00001733204,0.00009918042,0.0000437591,0.00008376933],"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":[0.000004911347,0.00001408832,0.0004473139,0.00003335368,0.000006240194,0.000006603433,0.0001927912,0.9939976,0.003459565,0.0001634109,0.00008081426,0.001593255],"study_design_scores_gemma":[0.0002688177,0.00006286396,0.0005338943,0.00002301209,0.00001504377,0.00006303385,0.00005004514,0.9896604,0.009050595,0.00006825794,0.00005140828,0.0001526046],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4262176,0.00009694305,0.5730523,0.000005770035,0.00005913692,0.0001108255,8.744168e-7,0.0001077514,0.0003487869],"genre_scores_gemma":[0.9664898,0.0002865587,0.03305491,0.00003179895,0.00003162436,0.000002458564,0.00002350611,0.00002726736,0.00005210845],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5402722,"threshold_uncertainty_score":0.4995175,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01155958416469444,"score_gpt":0.2316632427094211,"score_spread":0.2201036585447267,"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."}}