{"id":"W2169866651","doi":"10.1109/im.2001.924426","title":"A nearest neighbor method for efficient ICP","year":2002,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":103,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Constraint (computer-aided design); Triangle inequality; k-nearest neighbors algorithm; Point (geometry); Mathematics; Iterative closest point; Algorithm; Combinatorics; Set (abstract data type); Iterative method; Tree (set theory); Computational geometry; Computer science; Point cloud; Geometry; Artificial intelligence","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":[0.00004822141,0.00006403182,0.00007058744,0.00003341801,0.00003096724,0.00002393163,0.00003935292,0.00003616782,0.0002613435],"category_scores_gemma":[0.00001826368,0.00005685074,0.0000360983,0.00008487982,0.00000426425,0.00001349699,0.000003925486,0.00002793247,0.00005619959],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001835168,"about_ca_system_score_gemma":0.000001235807,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000568111,"about_ca_topic_score_gemma":0.000004216312,"domain_scores_codex":[0.9996297,0.000006149857,0.00009554767,0.0000790921,0.00005748503,0.000132009],"domain_scores_gemma":[0.9997718,0.00005623808,0.00000626881,0.000100613,0.00002401204,0.00004105565],"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":[9.069528e-7,0.00001308595,0.00000884278,0.00001795983,0.000006461705,5.210637e-7,0.00004815525,0.9828876,0.0008421837,0.004652331,0.005860646,0.005661315],"study_design_scores_gemma":[0.0001866769,0.00001724548,0.00003457775,0.00000346763,0.000006405694,8.728169e-7,0.00001079738,0.9820815,0.00265828,0.00004368331,0.01487572,0.00008078794],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001693759,0.00006431272,0.9862415,0.0001232876,0.0001686436,0.0001399098,0.00000352697,0.0001859104,0.01137913],"genre_scores_gemma":[0.788067,0.00001812616,0.2097761,0.0002317445,0.0001323985,0.00002332492,0.00001198721,0.00004778724,0.00169161],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7863732,"threshold_uncertainty_score":0.2861528,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02092999473290028,"score_gpt":0.2379874021519077,"score_spread":0.2170574074190074,"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."}}