{"id":"W3217751284","doi":"10.1109/lra.2021.3130648","title":"Direct Sparse Odometry With Planes","year":2021,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Odometry; Artificial intelligence; Computer science; Plane (geometry); Pose; Computer vision; Visual odometry; Artificial neural network; Segmentation; Algorithm; Mathematics; Robot; Geometry; 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":[0.00004064888,0.0001131101,0.0001289182,0.00007298665,0.00005704349,0.00008659682,0.00003082009,0.00004253078,0.00001199403],"category_scores_gemma":[0.000007448951,0.0001048837,0.00001925327,0.0001944459,0.00002356559,0.0000860806,0.000004553222,0.00006025971,0.00001098906],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002057567,"about_ca_system_score_gemma":0.000008498191,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002855899,"about_ca_topic_score_gemma":0.000006158885,"domain_scores_codex":[0.9994497,0.00001705035,0.0001364614,0.0001278521,0.0001293573,0.0001395195],"domain_scores_gemma":[0.9997285,0.00004259947,0.00002384132,0.0001206509,0.00003141149,0.00005300897],"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":[0.000001423462,0.000008362014,0.0005807768,0.00005520802,0.00002854815,0.00003352238,0.00005742236,0.9841591,0.0129717,0.0002443323,0.001318735,0.0005408791],"study_design_scores_gemma":[0.0003554806,0.00001734763,0.003408951,0.00006371488,0.0000357937,0.00003835706,0.00003014502,0.9765342,0.01824351,0.00001565807,0.001004969,0.0002518607],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4062929,0.0001863626,0.5903453,0.001318929,0.0005130702,0.00009373471,0.00000861528,0.000397877,0.0008432272],"genre_scores_gemma":[0.9888133,0.00007942127,0.01034318,0.0005503126,0.00008624126,0.000002427485,0.00004846398,0.00002853106,0.00004807357],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5825204,"threshold_uncertainty_score":0.4277032,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008284449927900232,"score_gpt":0.1849438401600627,"score_spread":0.1766593902321625,"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."}}