{"id":"W4389359021","doi":"10.5194/isprs-annals-x-1-w1-2023-613-2023","title":"LIDAR-INERTIAL LOCALIZATION WITH GROUND CONSTRAINT IN A POINT CLOUD MAP","year":2023,"lang":"en","type":"article","venue":"ISPRS annals of the photogrammetry, remote sensing and spatial information sciences","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Odometry; Inertial measurement unit; Lidar; Point cloud; Artificial intelligence; Computer vision; Computer science; Mean squared error; Ranging; Trajectory; Simultaneous localization and mapping; Sensor fusion; Factor graph; Matching (statistics); Robot; Remote sensing; Mobile robot; Mathematics; Geography; Algorithm; 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":[0.000463335,0.001150394,0.0008393124,0.001207627,0.0005870138,0.001208039,0.001440519,0.000873488,0.001691811],"category_scores_gemma":[0.001999822,0.0004729755,0.0008333081,0.002450677,0.0005762475,0.00158272,0.002010541,0.001012122,0.00139866],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006696443,"about_ca_system_score_gemma":0.001299787,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01719986,"about_ca_topic_score_gemma":0.01800768,"domain_scores_codex":[0.9990362,0.0001380479,0.00003594997,0.000288598,0.0004022916,0.00009891857],"domain_scores_gemma":[0.9994985,0.00008054881,0.0000654188,0.0001557771,0.000168654,0.00003117843],"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":[0.0004188219,0.0002045909,0.004507121,0.0002978197,0.0001625836,0.0004494542,0.000159102,0.6587315,0.02358457,0.009027016,0.009172416,0.2932851],"study_design_scores_gemma":[0.000020099,0.00005242576,0.001801351,0.00001656752,0.0000154033,0.00009402312,0.00006083634,0.981506,0.008160695,0.004270513,0.003987127,0.00001502083],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05442081,0.0003304258,0.938632,0.0001834768,0.00009469414,0.0001053865,0.00152433,0.002995911,0.00171295],"genre_scores_gemma":[0.6565141,0.0002962991,0.3347993,0.00009997439,0.00006155192,0.000178688,0.00530722,0.0002476447,0.002495161],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01719986,"threshold_uncertainty_score":0.03419954,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0284627718681873,"score_gpt":0.2592806291484798,"score_spread":0.2308178572802925,"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."}}