{"id":"W3180277944","doi":"10.1007/s10514-021-09992-7","title":"VIR-SLAM: visual, inertial, and ranging SLAM for single and multi-robot systems","year":2021,"lang":"en","type":"article","venue":"Autonomous Robots","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":80,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal","funders":"Fonds de recherche du Québec – Nature et technologies; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Odometry; Ranging; Computer science; Artificial intelligence; Computer vision; Simultaneous localization and mapping; Robot; Transformation (genetics); Visual odometry; Inertial measurement unit; Inertial frame of reference; Inertial navigation system; Monocular; Mobile robot","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":[0.00009755568,0.0002014291,0.0002819908,0.00008159912,0.0001315881,0.0001877109,0.0000472418,0.000123121,0.000004852935],"category_scores_gemma":[0.00004844604,0.0002193821,0.00004295171,0.0001288926,0.00003196425,0.0001215229,0.00003300484,0.00008178334,0.000004479527],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006284776,"about_ca_system_score_gemma":0.00002624966,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003274743,"about_ca_topic_score_gemma":0.00002883596,"domain_scores_codex":[0.999015,0.00002684058,0.0002884164,0.0002826576,0.00008507543,0.0003020011],"domain_scores_gemma":[0.9995382,0.00008395695,0.00003931449,0.0001574096,0.00008295035,0.00009820499],"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":[0.000005438053,0.00004707465,0.0002058707,0.0002668155,0.0000525728,0.00001516708,0.0003041411,0.8682009,0.125969,0.0004949801,0.0001650659,0.00427295],"study_design_scores_gemma":[0.0008679037,0.00005377454,0.0003730508,0.00007787788,0.00004317892,0.00004570916,0.0001083991,0.9833797,0.0133376,0.00001084609,0.001416208,0.0002857726],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1643917,0.00642011,0.8265723,0.0001418169,0.0009975799,0.0005533961,0.00001491997,0.0004382109,0.0004700098],"genre_scores_gemma":[0.9897982,0.000151308,0.009323351,0.00005470024,0.0001319846,0.00003069274,0.00005697886,0.00007384533,0.0003789662],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8254065,"threshold_uncertainty_score":0.8946142,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01994246764415938,"score_gpt":0.230582018392429,"score_spread":0.2106395507482696,"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."}}