{"id":"W2442645251","doi":"10.5194/isprs-archives-xli-b5-533-2016","title":"IMPROVED REAL-TIME SCAN MATCHING USING CORNER FEATURES","year":2016,"lang":"en","type":"article","venue":"The international 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":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Iterative closest point; Initialization; Computer science; Matching (statistics); Ranging; Convergence (economics); Artificial intelligence; Computer vision; Range (aeronautics); Iterative method; Algorithm; Iterative and incremental development; Blossom algorithm; Mathematics; Engineering; Point cloud","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001031703,0.000445915,0.0004269862,0.000834916,0.001086789,0.0006934381,0.001294867,0.0001103165,0.00001093674],"category_scores_gemma":[0.0004020477,0.0002575937,0.0003371477,0.0007694618,0.002152669,0.0005480387,0.0005696389,0.0003255129,0.000003494265],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008062249,"about_ca_system_score_gemma":0.0001751269,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.6759884,"about_ca_topic_score_gemma":0.0980835,"domain_scores_codex":[0.9961299,0.000218578,0.00132064,0.0003563332,0.001426056,0.0005484895],"domain_scores_gemma":[0.9971786,0.0008666999,0.001052255,0.0004648376,0.000272214,0.0001654199],"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":[0.00009157666,0.00001042722,0.00008761355,0.00003792205,0.00008963707,2.613017e-7,0.001785479,0.0283298,0.03475189,0.000009747196,0.00004616658,0.9347595],"study_design_scores_gemma":[0.0007008787,0.00008266741,0.001585626,0.0004464296,0.00004570523,0.0001125413,0.0008240496,0.9741248,0.01704061,0.003751083,0.0009449303,0.0003406988],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02600398,0.00002315289,0.9629501,0.00175217,0.001757392,0.000536497,0.0001101508,0.00009151433,0.00677505],"genre_scores_gemma":[0.9942358,0.0002349461,0.004726906,0.000466073,0.0001683568,2.529665e-7,0.00002882792,0.00002037278,0.0001185284],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9682317,"threshold_uncertainty_score":0.9999876,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01219111332560076,"score_gpt":0.2348504785225239,"score_spread":0.2226593651969231,"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."}}