{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003008067,0.0006330651,0.0005486385,0.0007150467,0.0002342988,0.0006250893,0.0005064696,0.0004295073,0.001441779],"category_scores_gemma":[0.0008775669,0.0003317296,0.0003506328,0.000601307,0.0003114488,0.001067014,0.0005252518,0.0003293596,0.0008311768],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003225456,"about_ca_system_score_gemma":0.0005962967,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00270113,"about_ca_topic_score_gemma":0.002979865,"domain_scores_codex":[0.9995956,0.00005454873,0.0000138025,0.00007549451,0.0002344934,0.00002603515],"domain_scores_gemma":[0.9998017,0.00004432363,0.00004811432,0.0000212372,0.00007341232,0.00001124175],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002116816,0.00008940413,0.002900744,0.0004369424,0.00007902297,0.0002769192,0.0001800266,0.1157061,0.1282872,0.01016519,0.002106392,0.7395603],"study_design_scores_gemma":[0.0000659295,0.0004522851,0.006229588,0.00009181363,0.00007987062,0.0009449996,0.0001497771,0.8691416,0.1004439,0.006920662,0.0153913,0.00008826381],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009951488,0.0003503402,0.9878727,0.00004676118,0.00003138511,0.00002084151,0.00002511621,0.0007695188,0.0009319031],"genre_scores_gemma":[0.4164185,0.000956894,0.576813,0.00006876593,0.00006384627,0.0001133666,0.0001579052,0.00007496438,0.005332803],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00270113,"threshold_uncertainty_score":0.005370736,"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."}}