{"id":"W2354819904","doi":"10.1109/acc.2016.7526532","title":"On the covariance of ICP-based scan-matching techniques","year":2016,"lang":"en","type":"preprint","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Iterative closest point; Covariance; Point (geometry); Intuition; Computer science; Lidar; Matching (statistics); Algorithm; Artificial intelligence; Mathematics; Mathematical optimization; Point cloud; Statistics; Remote sensing; Geography; Geometry","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":[0.0001853725,0.0001793767,0.0002013591,0.00007709199,0.00002985865,0.00002146313,0.0002301302,0.0001782069,0.0001367926],"category_scores_gemma":[0.00003492094,0.0001084361,0.00008138577,0.00004861651,0.00003223922,0.00001546344,0.00004396167,0.0002266183,0.00001543034],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006486292,"about_ca_system_score_gemma":0.00003898791,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004303789,"about_ca_topic_score_gemma":0.000007496213,"domain_scores_codex":[0.9992563,0.00003281502,0.0002461686,0.0001594807,0.0001753703,0.0001299111],"domain_scores_gemma":[0.9991215,0.0002291165,0.00006633966,0.0004960035,0.00006165267,0.00002536383],"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":[0.00000559619,0.00001246649,0.00000839013,0.000177655,0.00002692161,0.00000102592,0.00002422414,0.9275859,0.004558419,0.06395298,0.002100693,0.001545707],"study_design_scores_gemma":[0.0001566323,0.00003852976,0.00006687943,0.001484378,0.00002781725,4.534358e-7,0.00001187614,0.6426,0.2900639,0.06408481,0.001012349,0.0004523997],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003002805,0.00003346034,0.9786317,0.0005097077,0.0002520035,0.000283538,0.00004212027,0.0003701805,0.01687451],"genre_scores_gemma":[0.9895423,0.0000288258,0.009918164,0.0002202752,0.00007030764,0.00002865726,0.00001557833,0.0000468385,0.0001290686],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9865395,"threshold_uncertainty_score":0.4421894,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01363930803973998,"score_gpt":0.2225789528191348,"score_spread":0.2089396447793949,"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."}}