{"id":"W2510133201","doi":"10.1016/j.robot.2016.04.005","title":"The LiDAR compass: Extremely lightweight heading estimation with axis maps","year":2016,"lang":"en","type":"article","venue":"Robotics and Autonomous Systems","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Compass; Heading (navigation); Computer science; Lidar; Computer vision; Artificial intelligence; Cardinal direction; Remote sensing; Geodesy; Cartography; Geology; Geography","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":[0.0004107222,0.0008755152,0.000572483,0.0008172196,0.0002867182,0.0007531354,0.001173712,0.0005437577,0.005859791],"category_scores_gemma":[0.001378808,0.0006008272,0.0002630978,0.0007793694,0.000377414,0.001339208,0.00213418,0.0006917333,0.004482415],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001769828,"about_ca_system_score_gemma":0.0004720962,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00132729,"about_ca_topic_score_gemma":0.001791005,"domain_scores_codex":[0.9993197,0.00008646245,0.00002236248,0.00007043136,0.0004532136,0.00004783871],"domain_scores_gemma":[0.9995362,0.0001074337,0.00004366436,0.0001456436,0.0001281474,0.00003910202],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004594971,0.00005682807,0.001761531,0.0002043144,0.00006927042,0.0002023822,0.00009624879,0.01260309,0.05378604,0.00955998,0.02702375,0.8941771],"study_design_scores_gemma":[0.0003182151,0.0006693187,0.006773231,0.0001549407,0.0001464986,0.001869346,0.0001854099,0.6040766,0.1597795,0.02949576,0.1963006,0.0002305958],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00994125,0.0007411063,0.9722235,0.0001996203,0.0002146592,0.00006673148,0.000338798,0.01206601,0.004208397],"genre_scores_gemma":[0.2595454,0.001262513,0.718523,0.0003384234,0.0003008365,0.0002444637,0.001596842,0.001520508,0.01666806],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005859791,"threshold_uncertainty_score":0.01960295,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009639720749919391,"score_gpt":0.1884653410991835,"score_spread":0.1788256203492641,"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."}}