{"id":"W7105988518","doi":"10.1109/tits.2025.3632411","title":"DIDLM: A SLAM Dataset for Difficult Scenarios Featuring Infrared, Depth Cameras, LiDAR, 4D Radar, and Others Under Adverse Weather, Low Light Conditions, and Rough Roads","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Science Foundation of Shaanxi Province; National Natural Science Foundation of China","keywords":"RGB color model; Ground truth; Unmanned ground vehicle; Robustness (evolution); Sensor fusion; Robot; Adverse weather; Global Positioning System","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":[0.0007578987,0.003747827,0.001579595,0.001902131,0.001464581,0.001377101,0.004102568,0.002651527,0.006405666],"category_scores_gemma":[0.00306289,0.0007358309,0.002198477,0.002844599,0.0009035936,0.001869345,0.002681077,0.002953399,0.008884097],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001173001,"about_ca_system_score_gemma":0.001939597,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02859342,"about_ca_topic_score_gemma":0.08402217,"domain_scores_codex":[0.9981407,0.0002346065,0.000180227,0.000650641,0.0005273435,0.000266406],"domain_scores_gemma":[0.9983873,0.0002524237,0.0001294305,0.0005217836,0.0005381964,0.000170856],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0008227869,0.001128136,0.01446745,0.003780297,0.0006721775,0.0009390411,0.0004250988,0.06017416,0.01135535,0.002072924,0.8058881,0.09827437],"study_design_scores_gemma":[0.001121325,0.0008068157,0.06567881,0.0009596879,0.0002538501,0.001597116,0.002314184,0.2895046,0.02251781,0.007330908,0.6073226,0.0005922429],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.05595796,0.002227373,0.02818304,0.0009480018,0.001068171,0.0007344078,0.8709811,0.03039831,0.009501596],"genre_scores_gemma":[0.04173898,0.0002501066,0.02512492,0.0001842207,0.00004464229,0.0003137742,0.930752,0.0004253249,0.001166006],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02859342,"threshold_uncertainty_score":0.05685395,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01231856474669933,"score_gpt":0.245937414911448,"score_spread":0.2336188501647486,"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."}}