{"id":"W2766821096","doi":"10.1177/0278364917732639","title":"TSLAM: Tethered simultaneous localization and mapping for mobile robots","year":2017,"lang":"en","type":"article","venue":"The International Journal of Robotics Research","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute for Christian Studies; University of Toronto","funders":"","keywords":"Odometry; Robot; Simultaneous localization and mapping; Mobile robot; Computer science; Computer vision; Artificial intelligence; Trajectory; Block (permutation group theory); Particle filter; Point (geometry); Range (aeronautics); Bearing (navigation); Extended Kalman filter; Filter (signal processing); Kalman filter; Engineering; Mathematics; Physics","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":[0.001071537,0.00008800613,0.0001355655,0.000183229,0.0003469342,0.0004365702,0.0007399644,0.00006355262,0.00001036687],"category_scores_gemma":[0.0008041682,0.00006627091,0.00005521804,0.00005609331,0.0001382451,0.000157889,0.00008862866,0.0002601785,0.000003901238],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001122477,"about_ca_system_score_gemma":0.00004834358,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001362252,"about_ca_topic_score_gemma":0.00001184757,"domain_scores_codex":[0.9986951,0.00004760443,0.0003175134,0.00008548324,0.0006575516,0.000196756],"domain_scores_gemma":[0.9976872,0.0006019031,0.0001351296,0.0002145905,0.001292528,0.00006865554],"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":[0.00004026457,0.00001856063,0.0001875093,0.00002205552,0.00009477611,0.00001999122,0.0002540184,0.9854401,0.002159274,0.001601467,0.0007409878,0.009421018],"study_design_scores_gemma":[0.0005010962,0.00008725306,0.00014276,0.000091207,0.00001007324,0.0000506753,0.000207501,0.9914885,0.001340519,0.002856137,0.003147758,0.00007652117],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02846978,0.0005347251,0.9670909,0.001699061,0.001117978,0.0003567494,0.00000926693,0.00002112564,0.0007004365],"genre_scores_gemma":[0.9938833,0.00066336,0.004709007,0.00003308362,0.0004541052,0.000005211637,0.000004963271,0.0000289559,0.000218007],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9654135,"threshold_uncertainty_score":0.4209856,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07070047902891316,"score_gpt":0.3641038505264088,"score_spread":0.2934033714974956,"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."}}