{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002387383,0.0008323622,0.000665369,0.0006386951,0.0002759255,0.0009792486,0.001112428,0.0006786123,0.002051337],"category_scores_gemma":[0.001089495,0.0006045002,0.0006434813,0.001100382,0.0008891464,0.001707554,0.002632191,0.001164627,0.0006142591],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004436961,"about_ca_system_score_gemma":0.0008437611,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006272191,"about_ca_topic_score_gemma":0.008238345,"domain_scores_codex":[0.9995645,0.00005876617,0.00002015694,0.0001170042,0.0001997157,0.00003985278],"domain_scores_gemma":[0.9997332,0.00005488988,0.00004132804,0.0000817145,0.00007267027,0.00001613898],"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":[0.00009651673,0.00004415189,0.0009049489,0.0001302072,0.00009252149,0.00008582215,0.0001448095,0.6198729,0.02214382,0.07179256,0.003708638,0.2809831],"study_design_scores_gemma":[0.00001403717,0.00003608632,0.000215666,0.00001161636,0.00001262175,0.00005047063,0.00001990693,0.9678878,0.00505789,0.02211943,0.004562086,0.00001234356],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002701311,0.00004445594,0.9960789,0.00004796758,0.00001979907,0.000009388073,0.00004572304,0.0002220737,0.0008303633],"genre_scores_gemma":[0.351341,0.0003442091,0.6423237,0.0002293455,0.0001153184,0.0001251681,0.000538543,0.0001978582,0.004784888],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006272191,"threshold_uncertainty_score":0.01247138,"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."}}