{"id":"W4283025710","doi":"10.3389/frobt.2022.801886","title":"Multi-Session Visual SLAM for Illumination-Invariant Re-Localization in Indoor Environments","year":2022,"lang":"en","type":"article","venue":"Frontiers in Robotics and AI","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Session (web analytics); Artificial intelligence; Simultaneous localization and mapping; Computer vision; Scale-invariant feature transform; Robot; Mobile robot; Image (mathematics)","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001779957,0.0001217927,0.0001653477,0.0002301115,0.0001186834,0.00002719989,0.0000679861,0.00007067356,0.000008818111],"category_scores_gemma":[0.00002638467,0.0001401566,0.0000240989,0.0001770086,0.00002383569,0.00008770273,0.00003848374,0.0001424302,4.324477e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001830621,"about_ca_system_score_gemma":0.00001248147,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001254728,"about_ca_topic_score_gemma":0.00001825955,"domain_scores_codex":[0.999149,0.00004192123,0.0002735003,0.0001917435,0.0001464301,0.0001973823],"domain_scores_gemma":[0.9998031,0.00002108849,0.0000367447,0.00008782306,0.000009781344,0.00004142241],"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":[0.00002060938,0.00008867325,0.01507902,0.00003560856,0.000007545355,0.000004499903,0.0003525601,0.9815096,0.0002602759,0.000237556,0.0009605695,0.001443478],"study_design_scores_gemma":[0.001110985,0.0000790245,0.002189478,0.00002181851,0.000008515809,6.674136e-7,0.0003886456,0.9933938,0.0001428728,0.0002524093,0.002242341,0.0001694623],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006584885,0.0002183378,0.9918653,0.0001582236,0.0007532597,0.0003578826,0.00001390741,0.00002443208,0.00002375821],"genre_scores_gemma":[0.9646655,0.0001770069,0.03452202,0.000188101,0.00003239047,0.0000647061,0.0002062555,0.00004054598,0.0001034705],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9580806,"threshold_uncertainty_score":0.571542,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00969244036841209,"score_gpt":0.2283807389435654,"score_spread":0.2186882985751533,"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."}}