{"id":"W3164138238","doi":"10.1109/crv52889.2021.00031","title":"Self-Calibration of the Offset Between GPS and Semantic Map Frames for Robust Localization","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Global Positioning System; Computer science; Offset (computer science); Computer vision; Artificial intelligence; Frame (networking); Lidar; Convolutional neural network; Reference frame; Remote sensing; Geography; Telecommunications","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001050282,0.0001789433,0.0002728871,0.00006111469,0.00005668615,0.00009631141,0.0001095139,0.0003113588,0.00001497355],"category_scores_gemma":[0.00003008694,0.0001469343,0.00008590291,0.00009963129,0.00002465756,0.00006219133,0.0001016032,0.0001523703,3.082878e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003394276,"about_ca_system_score_gemma":0.00004044219,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002001674,"about_ca_topic_score_gemma":0.0000303943,"domain_scores_codex":[0.9991089,0.00004123482,0.0003571845,0.0002150446,0.0001573936,0.0001202457],"domain_scores_gemma":[0.9993805,0.00007954695,0.00008905382,0.0003010758,0.0001147287,0.00003505053],"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":[0.000001177507,0.00001143325,0.00335516,0.001708148,0.00007899383,1.324394e-7,0.0001573128,0.9927004,0.00005167526,0.000755056,0.0008426568,0.0003378353],"study_design_scores_gemma":[0.0001431161,0.000009894237,0.0007584834,0.0001735327,0.000124097,3.700436e-7,0.00007269705,0.9936086,0.003938189,0.0006364346,0.0003701761,0.0001644123],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01227994,0.0002496037,0.9859626,0.0002476776,0.000423869,0.0005610253,0.00004070065,0.0001408294,0.00009371569],"genre_scores_gemma":[0.9758703,0.0001652047,0.02283043,0.00006113895,0.0001475583,0.00002236894,0.0007844546,0.00005424996,0.00006427968],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9635904,"threshold_uncertainty_score":0.5991805,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01666264888517261,"score_gpt":0.2111938085227124,"score_spread":0.1945311596375398,"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."}}