{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006080244,0.000129873,0.0001679539,0.0003296883,0.0001652573,0.0001662118,0.0001077288,0.00006499769,0.000002754664],"category_scores_gemma":[0.00009845852,0.00009157087,0.00004250908,0.001203788,0.0002898443,0.0002118884,0.00003174023,0.00009701939,0.000003998211],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001485505,"about_ca_system_score_gemma":0.00003688629,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03053627,"about_ca_topic_score_gemma":0.007658279,"domain_scores_codex":[0.9988347,0.0000461054,0.0004013362,0.000107608,0.0003595017,0.0002507335],"domain_scores_gemma":[0.9994984,0.00005977129,0.0001201652,0.0001355412,0.0001299423,0.00005614182],"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":[0.00002044473,0.000004687446,0.0002826772,0.00007224145,0.00001071102,0.000001163855,0.001057528,0.5861811,0.0004847793,0.00003653761,0.000271937,0.4115762],"study_design_scores_gemma":[0.0002076592,0.00007023007,0.001169167,0.0001362488,0.00000501401,0.000008051727,0.0008487711,0.9890878,0.007316676,0.0003475295,0.0006756594,0.0001272195],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2114049,0.00002156051,0.7865048,0.0008079029,0.0004146439,0.0002233181,0.000007487184,0.00009741381,0.0005179422],"genre_scores_gemma":[0.9992032,0.00006553595,0.0004052574,0.0002628026,0.00003538107,1.653607e-7,0.0000141143,0.000007498765,0.000006017675],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7877983,"threshold_uncertainty_score":0.9759195,"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."}}