{"id":"W4389765434","doi":"10.5194/isprs-archives-xlviii-1-w2-2023-875-2023","title":"LIDAR-INERTIAL NAVIGATION BASED ON MAP AIDED DISTANCE CONSTRAINT AND FACTOR GRAPH OPTIMIZATION","year":2023,"lang":"en","type":"article","venue":"˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Lidar; Odometry; Inertial measurement unit; Computer science; Simultaneous localization and mapping; Artificial intelligence; Factor graph; Computer vision; Ranging; Point cloud; Inertial navigation system; Constraint (computer-aided design); Remote sensing; Geography; Mathematics; Robot; Algorithm; Orientation (vector space); Mobile robot; Decoding methods","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":[0.0002800451,0.001071919,0.000772937,0.0008099535,0.0003791793,0.0006029107,0.0009485101,0.0006153402,0.002516966],"category_scores_gemma":[0.001018186,0.0003125675,0.000682888,0.001210244,0.0003726255,0.0008718499,0.0009883874,0.0005724546,0.0004924529],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006008415,"about_ca_system_score_gemma":0.001231045,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01534652,"about_ca_topic_score_gemma":0.01538182,"domain_scores_codex":[0.9995589,0.0000691941,0.00001853294,0.0001233274,0.00017769,0.00005235772],"domain_scores_gemma":[0.999733,0.00006366243,0.00004296444,0.00004012839,0.00009886586,0.00002120838],"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":[0.0001194713,0.00006753037,0.0008685709,0.0001034683,0.00005021634,0.00009957127,0.0000696584,0.7868501,0.00707614,0.007134374,0.003951036,0.1936099],"study_design_scores_gemma":[0.000008145238,0.00002136862,0.0001540075,0.000002795977,0.000004701441,0.00001422762,0.00001076752,0.9965952,0.001080216,0.001235774,0.000867107,0.000005787705],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0219711,0.0001607021,0.9740241,0.00008858935,0.00005542796,0.00005346247,0.0001463697,0.0009971627,0.002503108],"genre_scores_gemma":[0.5570012,0.0001899328,0.4377797,0.0001144292,0.00005180332,0.000182624,0.001178063,0.0002240597,0.003278174],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01534652,"threshold_uncertainty_score":0.03051442,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01438278928585821,"score_gpt":0.2367921056376815,"score_spread":0.2224093163518233,"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."}}