{"id":"W4404103071","doi":"10.1109/jiot.2024.3492913","title":"LiDAR-Based Multisensor Fusion With 3-D Digital Maps for High-Precision Positioning","year":2024,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":13,"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; Computer science; Sensor fusion; Remote sensing; Fusion; Computer vision; Artificial intelligence; Real-time computing; Geology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003368576,0.0007343038,0.0004497238,0.001265151,0.0002894168,0.0007093814,0.0006836841,0.0005535962,0.001930999],"category_scores_gemma":[0.0008155443,0.0002764943,0.0005507701,0.001933919,0.0002318933,0.001391663,0.001598766,0.0005296473,0.001407175],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003179068,"about_ca_system_score_gemma":0.0005814335,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001988492,"about_ca_topic_score_gemma":0.00288376,"domain_scores_codex":[0.9994906,0.00008068771,0.00002423479,0.00009171857,0.0002708582,0.0000419596],"domain_scores_gemma":[0.9996765,0.00004209218,0.00004040155,0.0001056265,0.0001239775,0.00001150111],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002359426,0.0001340935,0.004089882,0.0004501241,0.0001515072,0.000353381,0.000425873,0.09265698,0.1744684,0.006306008,0.007353848,0.7133739],"study_design_scores_gemma":[0.00006145216,0.0003272709,0.01175814,0.00009449197,0.0001359578,0.0005113147,0.0004418968,0.7529256,0.1759448,0.01149542,0.04613603,0.000167551],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05522718,0.0008283372,0.9337272,0.0002219304,0.0002191449,0.00007274093,0.0007239435,0.00319458,0.005784954],"genre_scores_gemma":[0.6700075,0.000590951,0.3245041,0.000183586,0.00009221725,0.0001045482,0.001443951,0.0001490177,0.002924173],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001988492,"threshold_uncertainty_score":0.006459892,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007719276964772814,"score_gpt":0.2150836709589033,"score_spread":0.2073643939941305,"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."}}