{"id":"W4387682177","doi":"10.1109/tro.2023.3323938","title":"Present and Future of SLAM in Extreme Environments: The DARPA SubT Challenge","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Robotics","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":178,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Defense Advanced Research Projects Agency; National Aeronautics and Space Administration","keywords":"Simultaneous localization and mapping; Robotics; Robot; Artificial intelligence; Competition (biology); Computer science; Search and rescue; Drone; Field (mathematics); Engineering; Mobile 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007109412,0.000107975,0.0001146542,0.0001136528,0.00005097274,0.00001049561,0.00007190509,0.00007728288,0.00001873826],"category_scores_gemma":[6.131475e-7,0.00009128275,0.00003561325,0.0002330736,0.00003973834,0.0000445007,8.707574e-7,0.0001591715,0.00001292359],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000295582,"about_ca_system_score_gemma":0.000004871978,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008285521,"about_ca_topic_score_gemma":0.00003347927,"domain_scores_codex":[0.9993779,0.00002535654,0.0001838704,0.0001118672,0.0001473296,0.000153686],"domain_scores_gemma":[0.999681,0.00005728256,0.00001713312,0.0002025679,0.00000732618,0.00003469968],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00000429831,0.00004320759,0.00001620744,0.00003302463,0.00001880911,0.000003307983,0.0003463824,0.9927337,0.0004501234,0.0002242108,0.00007340337,0.006053278],"study_design_scores_gemma":[0.0003265779,0.00004868306,0.0008511576,0.00002504402,0.00002283226,0.000001614096,0.000331996,0.9937835,0.002713004,0.0001715373,0.001595986,0.0001280831],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01219638,0.000347067,0.9835091,0.002164315,0.0008027779,0.0003179676,0.00001943607,0.0001235273,0.0005193996],"genre_scores_gemma":[0.9955087,0.003512552,0.0006396178,0.0000179937,0.00008700136,0.0000122905,0.00000537402,0.00003282415,0.0001836343],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9833123,"threshold_uncertainty_score":0.3722402,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03202102561179112,"score_gpt":0.2121457210149481,"score_spread":0.180124695403157,"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."}}