{"id":"W2974713248","doi":"10.5194/isprs-archives-xlii-2-w16-251-2019","title":"INS-AIDED 3D LIDAR SEAMLESS MAPPING IN CHALLENGING ENVIRONMENT FOR FUTURE HIGH DEFINITION MAP","year":2019,"lang":"en","type":"article","venue":"The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"GNSS applications; Odometry; Lidar; Computer science; Simultaneous localization and mapping; Global Map; Inertial navigation system; Global Positioning System; Sensor fusion; Process (computing); Computer vision; Remote sensing; Artificial intelligence; Real-time computing; Geography; Inertial frame of reference; Robot; Mobile robot; Telecommunications","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":[0.0003234387,0.0006715427,0.0003727525,0.0008210815,0.0005441,0.001062563,0.0006925298,0.000566003,0.002732544],"category_scores_gemma":[0.0005245674,0.0003078572,0.0004556387,0.0008784592,0.0002571763,0.001227969,0.001240928,0.0006495737,0.001486472],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003078925,"about_ca_system_score_gemma":0.001037401,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003881149,"about_ca_topic_score_gemma":0.005039443,"domain_scores_codex":[0.999652,0.00005569071,0.00001206835,0.00006332897,0.000169779,0.00004705236],"domain_scores_gemma":[0.9997708,0.00002176846,0.00002159763,0.00005750608,0.000109326,0.00001902401],"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":[0.0003317891,0.0001509777,0.0120184,0.0004078481,0.0001312912,0.0009545896,0.0006448299,0.2332978,0.0945835,0.009490339,0.01798633,0.6300022],"study_design_scores_gemma":[0.00003585315,0.0000762485,0.005319296,0.00003346978,0.00004166665,0.0002311616,0.000441815,0.9386029,0.02727237,0.004655363,0.02323516,0.00005471272],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1015344,0.0006138378,0.8813882,0.0005097958,0.0002613078,0.0000906866,0.000764225,0.004801086,0.01003631],"genre_scores_gemma":[0.717545,0.0004532861,0.2739431,0.0001759074,0.00006173824,0.00009867003,0.001680197,0.0002544565,0.005787655],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003881149,"threshold_uncertainty_score":0.009141266,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01494385580707768,"score_gpt":0.2291810739202641,"score_spread":0.2142372181131864,"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."}}