{"id":"W2204930965","doi":"10.1109/iros.2015.7353368","title":"Full STEAM ahead: Exactly sparse gaussian process regression for batch continuous-time trajectory estimation on SE(3)","year":2015,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":104,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Trajectory; Gaussian process; Interpolation (computer graphics); Gaussian; Computer science; Process (computing); Regression; Kriging; Algorithm; Singularity; Mathematics; Applied mathematics; Artificial intelligence; Statistics; Mathematical analysis; Machine learning","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001941368,0.0002067926,0.0002286344,0.0001094197,0.00005669802,0.00005161161,0.00009496036,0.0001425804,0.00006122805],"category_scores_gemma":[0.00006696012,0.0001720641,0.00004974737,0.0001277456,0.00001699223,0.0001666333,0.00000568525,0.00009821488,0.0001095026],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001033723,"about_ca_system_score_gemma":0.00004207954,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000101588,"about_ca_topic_score_gemma":0.00001197892,"domain_scores_codex":[0.9989787,0.00002534419,0.0002745313,0.0002177714,0.00024536,0.0002582376],"domain_scores_gemma":[0.9993826,0.00005873861,0.0000525262,0.0002142813,0.0001260415,0.0001658013],"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":[0.0001320897,0.00006782871,0.00003677965,0.0001194095,0.00001649443,0.000002658581,0.0004400155,0.9795383,0.002646063,0.0002933404,0.01082187,0.005885175],"study_design_scores_gemma":[0.0009197115,0.0003556602,0.00006932925,0.0001144041,0.000020821,0.000003574079,0.0001653317,0.9822309,0.01383884,0.0002475896,0.001776115,0.0002577346],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4140885,0.00009899338,0.5586953,0.0002953068,0.0007255179,0.001212922,0.00002416744,0.001089949,0.02376929],"genre_scores_gemma":[0.9901375,0.00000567542,0.007365049,0.00008692973,0.0001507753,0.00004471821,0.0001679692,0.0000763723,0.001965017],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.576049,"threshold_uncertainty_score":0.7016572,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02882001600036325,"score_gpt":0.266830566216144,"score_spread":0.2380105502157808,"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."}}