{"id":"W4389739137","doi":"10.5194/isprs-archives-xlviii-1-w2-2023-909-2023","title":"INVESTIGATING THE COMPLEMENTARY USE OF RADAR AND LIDAR FOR POSITIONING APPLICATIONS","year":2023,"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":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Military College of Canada; Microsemi (Canada); Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Lidar; Odometry; Point cloud; Computer science; Radar; Remote sensing; Artificial intelligence; Computer vision; Ranging; Inertial measurement unit; Heading (navigation); Process (computing); Inertial navigation system; Orientation (vector space); Geography; Mobile robot; Geodesy; Robot; Telecommunications; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.001033207,0.0002871016,0.0003042742,0.0006486161,0.001312146,0.0005634762,0.0008420648,0.00006007681,0.000002291117],"category_scores_gemma":[0.0004164474,0.0001812553,0.0002174563,0.0009358521,0.002180226,0.0003911199,0.0004656332,0.0002574129,9.533293e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002899283,"about_ca_system_score_gemma":0.0001002531,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.3953978,"about_ca_topic_score_gemma":0.05326609,"domain_scores_codex":[0.9970315,0.0001555652,0.001156987,0.000249345,0.001045175,0.0003614246],"domain_scores_gemma":[0.9969068,0.001533708,0.0008789456,0.000332535,0.0002483087,0.00009966891],"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":[0.0000349059,0.000007256797,0.0002352687,0.00008257684,0.00008193398,6.116814e-8,0.002560181,0.04508994,0.003802331,0.00004559261,0.00006987427,0.9479901],"study_design_scores_gemma":[0.0004735227,0.00007463941,0.002580744,0.0002730742,0.00004559323,0.00003758335,0.001714956,0.9782389,0.006330125,0.006667038,0.003364354,0.0001994973],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01660535,0.00002797205,0.9775831,0.002763588,0.00074305,0.0008651247,0.0002010235,0.00006090332,0.001149967],"genre_scores_gemma":[0.9907594,0.000203946,0.008148602,0.0006390339,0.00009954144,0.000001070065,0.0001090997,0.00001301266,0.00002624745],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9741541,"threshold_uncertainty_score":0.999988,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02761305122088905,"score_gpt":0.2597567641161833,"score_spread":0.2321437128952943,"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."}}