{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000632267,0.0009951128,0.0007901505,0.001475586,0.0007259941,0.0009487311,0.001553059,0.0007647058,0.005176333],"category_scores_gemma":[0.001799492,0.0007338386,0.0008465854,0.002166595,0.000410608,0.001124067,0.001775607,0.001520487,0.005667537],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004994391,"about_ca_system_score_gemma":0.00221309,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01097789,"about_ca_topic_score_gemma":0.01154317,"domain_scores_codex":[0.9990651,0.00006033732,0.00003442687,0.0002327561,0.0004987912,0.0001086822],"domain_scores_gemma":[0.9995373,0.00005342876,0.000046112,0.0001071479,0.0002297455,0.00002622691],"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":[0.0001663493,0.000230817,0.002845197,0.0001146915,0.0001008807,0.0002548528,0.0003308345,0.1381711,0.1190215,0.006712717,0.01084544,0.7212057],"study_design_scores_gemma":[0.00003065519,0.0001078212,0.003563729,0.00001152185,0.00002223216,0.0001294716,0.00006702213,0.929916,0.04577205,0.003680333,0.01663197,0.00006718621],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005803829,0.00001966413,0.9854746,0.00003963711,0.00002049013,0.00008574608,0.0002102189,0.007587233,0.0007586545],"genre_scores_gemma":[0.1181652,0.00006907244,0.8751835,0.00004439707,0.00001929948,0.0002400019,0.001727209,0.001019483,0.003531693],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01097789,"threshold_uncertainty_score":0.021828,"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."}}