{"id":"W2182203767","doi":"10.1109/mmsp.2015.7340874","title":"An improved ICP registration algorithm with a weight-bootstrap scheme","year":2015,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"National Natural Science Foundation of China; Canada Research Chairs","keywords":"Iterative closest point; Algorithm; Matching (statistics); Point set registration; Computer science; Point (geometry); Iterative method; Quadratic equation; Metric (unit); Mathematics; Tangent; Iterative and incremental development; Mathematical optimization; Artificial intelligence; Statistics","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":[0.00007187944,0.0001056582,0.0000841485,0.00004011206,0.00002361036,0.00005877784,0.00005937962,0.00006281036,0.00002052406],"category_scores_gemma":[0.00000327028,0.00008600503,0.0000135286,0.0001148037,0.00001596078,0.0001899027,0.000001889497,0.00006359224,0.00001440476],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003417058,"about_ca_system_score_gemma":0.0000344259,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004878096,"about_ca_topic_score_gemma":0.00007309546,"domain_scores_codex":[0.9994674,0.000009193801,0.0001262284,0.0001255727,0.0001310589,0.0001405164],"domain_scores_gemma":[0.9995548,0.000004416784,0.00001768278,0.0002032499,0.0000859941,0.0001338673],"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":[0.0000769516,0.0002457664,0.001089378,0.00009404503,0.0001473051,0.00004479989,0.000806785,0.8520693,0.06097347,0.02281654,0.007681797,0.05395386],"study_design_scores_gemma":[0.00042561,0.0001561044,0.00008348902,0.000005626779,0.000007046329,0.000006049947,0.00007474303,0.9903212,0.007276895,0.0001991018,0.00129297,0.000151124],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01614966,0.00002854475,0.9766179,0.00006715829,0.0001059173,0.0001166399,0.000002220566,0.0003734601,0.006538445],"genre_scores_gemma":[0.7962595,0.0000116246,0.2028996,0.00005932155,0.0001769346,0.000008902073,0.00009485666,0.00004279419,0.0004464146],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7801099,"threshold_uncertainty_score":0.3507183,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02074730452216905,"score_gpt":0.2304314126835172,"score_spread":0.2096841081613482,"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."}}