{"id":"W2097648282","doi":"10.1109/im.2001.924434","title":"The parallel iterative closest point algorithm","year":2002,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Computer science; Overhead (engineering); Iterative method; Algorithm; Parallel computing; Image (mathematics); Load balancing (electrical power); Transformation (genetics); Point (geometry); Node (physics); Artificial intelligence; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00141828,0.001170614,0.001962145,0.001269376,0.001145215,0.001957828,0.003095827,0.001362581,0.005589591],"category_scores_gemma":[0.00465715,0.0008328258,0.001255844,0.001922052,0.0008340561,0.001907584,0.002648182,0.00178761,0.00448681],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006903526,"about_ca_system_score_gemma":0.002323064,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004671135,"about_ca_topic_score_gemma":0.003706216,"domain_scores_codex":[0.9976833,0.0004323269,0.0001166701,0.0003308503,0.001287656,0.0001491586],"domain_scores_gemma":[0.9984716,0.0003584742,0.0000986421,0.0003767398,0.000622092,0.00007250434],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002112312,0.00009986347,0.0008409464,0.0002662201,0.0001551309,0.0002136651,0.0001615691,0.371014,0.00678196,0.03040105,0.01111157,0.5787427],"study_design_scores_gemma":[0.00006791126,0.00007895911,0.0001934309,0.00002316201,0.00003124548,0.0003469416,0.00003391238,0.9454911,0.006049572,0.01948412,0.02815866,0.00004098533],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001033076,0.0001905798,0.9946837,0.00007448925,0.0000736772,0.00007016248,0.00003319796,0.001084778,0.002756274],"genre_scores_gemma":[0.04626528,0.0004983793,0.9469525,0.00008664616,0.0000924681,0.0002381042,0.0001990667,0.0002903081,0.005377338],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005589591,"threshold_uncertainty_score":0.01869911,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01084919594491101,"score_gpt":0.1880610159173326,"score_spread":0.1772118199724216,"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."}}