{"id":"W4389666170","doi":"10.1109/iros55552.2023.10341596","title":"Need for Speed: Fast Correspondence-Free Lidar-Inertial Odometry Using Doppler Velocity","year":2023,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Odometry; Lidar; Doppler effect; Angular velocity; Computer science; Gyroscope; Observability; Physics; Acoustics; Artificial intelligence; Optics; Mathematics; Mobile robot; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009554329,0.001110275,0.001046523,0.001146215,0.0009530458,0.001533681,0.00185916,0.0009546112,0.0051474],"category_scores_gemma":[0.006444194,0.0008612695,0.0006186344,0.001257458,0.0007008223,0.003879177,0.003890076,0.001387502,0.002695666],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005082287,"about_ca_system_score_gemma":0.001434222,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002896043,"about_ca_topic_score_gemma":0.003421526,"domain_scores_codex":[0.998399,0.000164963,0.00005658588,0.0002848834,0.0009689247,0.0001257778],"domain_scores_gemma":[0.9979427,0.0006055327,0.000183429,0.0006190794,0.0005662824,0.00008296511],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003749736,0.0001364869,0.004439775,0.000423218,0.0001262504,0.0002178729,0.0004795837,0.06187,0.05549891,0.03498403,0.009368755,0.8320801],"study_design_scores_gemma":[0.0001267185,0.0002667073,0.002655562,0.0001171092,0.00007431203,0.0006658779,0.0002642129,0.8669717,0.05183029,0.0337663,0.04315743,0.0001037641],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01084745,0.0003072092,0.983871,0.0001936866,0.0001917734,0.00005068297,0.00008364317,0.002677091,0.001777449],"genre_scores_gemma":[0.3099597,0.0004148824,0.6843143,0.0001838314,0.0001710641,0.0001732946,0.0003891294,0.0005023339,0.003891467],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0051474,"threshold_uncertainty_score":0.01721978,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03555785687382124,"score_gpt":0.2597629540280688,"score_spread":0.2242050971542475,"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."}}