{"id":"W3132558215","doi":"10.1109/iros45743.2020.9340944","title":"A Point Cloud Registration Pipeline using Gaussian Process Regression for Bathymetric SLAM","year":2020,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Research and Development","keywords":"Point cloud; Iterative closest point; Computer science; Artificial intelligence; Computer vision; Pipeline (software); Simultaneous localization and mapping; Feature (linguistics); Image registration; Gaussian process; Gaussian; Robot; Image (mathematics); Mobile robot","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":[0.00007437829,0.0001203778,0.0001303237,0.00007340148,0.00005896301,0.00004491403,0.00006324105,0.00007518089,0.00002621804],"category_scores_gemma":[0.0001015556,0.0001022587,0.00004397296,0.0004289876,0.000008122306,0.0001083602,0.000006020573,0.00006514197,0.000004900822],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003559991,"about_ca_system_score_gemma":0.00001982807,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006421501,"about_ca_topic_score_gemma":0.00000463416,"domain_scores_codex":[0.9992971,0.000009312126,0.0002456546,0.0001623813,0.0001322227,0.0001532991],"domain_scores_gemma":[0.9996673,0.00002294952,0.00004083387,0.0001012432,0.00007819598,0.00008940291],"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":[0.00003006304,0.0000143487,0.00007523158,0.0003087278,0.000008996376,0.000002706793,0.000268318,0.9815728,0.01005181,0.001897536,0.003609737,0.002159732],"study_design_scores_gemma":[0.0003323299,0.00004712434,0.00002090249,0.00003724577,0.00001617234,0.000002218348,0.00009940728,0.9840565,0.01433126,0.0001660753,0.0007497465,0.0001409657],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03254328,0.00008211863,0.9641147,0.0006148738,0.0001846023,0.0002906565,0.000004534505,0.0002593651,0.001905874],"genre_scores_gemma":[0.9859186,0.0000165093,0.01326069,0.000196007,0.0003763812,0.000006735204,0.00004900808,0.00003770884,0.000138328],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9533753,"threshold_uncertainty_score":0.4169987,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03627051428061948,"score_gpt":0.2673864184226617,"score_spread":0.2311159041420422,"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."}}