{"id":"W4389739819","doi":"10.5194/isprs-archives-xlviii-1-w2-2023-841-2023","title":"AN EVALUATION OF SOLID-STATE LIDAR FOR LOCALIZATION AND HD POINT CLOUD MAPPING","year":2023,"lang":"en","type":"article","venue":"˜The œinternational 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":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Lidar; GNSS applications; Computer science; Point cloud; Remote sensing; Inertial measurement unit; Mobile mapping; Sensor fusion; Global Positioning System; Real-time computing; Non-line-of-sight propagation; Simultaneous localization and mapping; Computer vision; Artificial intelligence; Geography; Mobile robot; Telecommunications; Wireless; 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":[0.001131709,0.0004306928,0.0003607238,0.0006652641,0.0003480776,0.0005378064,0.0006748246,0.0006628689,0.001212533],"category_scores_gemma":[0.001905919,0.0001593051,0.0002419988,0.000624289,0.0002412788,0.0009456176,0.0005327574,0.0002101266,0.0002764371],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004089695,"about_ca_system_score_gemma":0.0005485971,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004075083,"about_ca_topic_score_gemma":0.004245343,"domain_scores_codex":[0.9992086,0.0001998786,0.00003456974,0.00009599695,0.000387766,0.00007322813],"domain_scores_gemma":[0.9987245,0.0002979969,0.00008664019,0.0001609921,0.0006571098,0.00007275741],"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":[0.002097629,0.001332988,0.03734021,0.0008144867,0.0002670124,0.0005528823,0.0003756884,0.2509397,0.2542599,0.002709679,0.00231965,0.4469901],"study_design_scores_gemma":[0.00007832586,0.002101022,0.01071204,0.00002477691,0.00006937738,0.0001827526,0.00021145,0.8881598,0.09648636,0.0003101549,0.001625187,0.00003889826],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9257846,0.0004502787,0.06954066,0.0001448985,0.0000930181,0.0001446946,0.000219076,0.0006903984,0.002932328],"genre_scores_gemma":[0.9815849,0.00006636878,0.01793934,0.00001552177,0.000005249484,0.00002183409,0.00009709134,0.00001025195,0.0002593692],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004075083,"threshold_uncertainty_score":0.008102715,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02653457092489668,"score_gpt":0.2765444408685943,"score_spread":0.2500098699436976,"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."}}