[{"data":1,"prerenderedAt":609},["ShallowReactive",2],{"method-madicp2024":3},{"method":4,"reference":56,"equipment":79,"figures":114,"results":115},{"id":5,"label":6,"shortName":7,"title":8,"year":9,"era":10,"cluster":11,"scope":12,"keyIdeaZh":13,"keyIdeaEn":14,"fulltextStatus":15,"publicationStatus":16,"recommendation":17,"constructionRelevance":18,"validationEnvironment":19,"strengths":21,"limitations":26,"sensors":31,"platform":34,"estimator":38,"association":39,"timeModel":40,"deskew":41,"loopClosure":42,"globalOptimization":42,"mapRepresentation":43,"prior":42,"outputGeometry":44,"compute":45,"codeUrl":46,"codeLicense":47,"relatedVersions":48},"madicp2024","Ferrari et al., 2024","MAD-ICP","MAD-ICP: It is All About Matching Data - Robust and Informed LiDAR Odometry",2024,"recent","C05","odometry_with_local_mapping","MAD-ICP 將每次掃描建成以主成分分析（PCA）切分的 kd 樹，葉節點帶有平均位置與法向量，並以點到平面 ICP 對齊關鍵影格 kd 樹組成的局部地圖。局部地圖只在匹配比例低於門檻時更新，且依位姿共變異數的行列式（D-optimality）挑選不確定性最小的幀加入，以避免把雜訊持續注入地圖。作者主張在不同感測器與運動型態下不失敗，並以同一型號 LiDAR 使用同一組參數。","Point-to-plane LiDAR odometry over PCA kd-trees whose local map is updated information-aware, adding only the least-uncertain keyframe when scan support drops.","full_text_reviewed","peer_reviewed_published","supplementary","not_reported（Table II 將所用 Hilti 2021 兩序列標示為 drone 與 lab，分別以四旋翼與手持方式、Ouster OS0-64 取得；依官方序列頁對應為 RPG Drone Testing Arena 與 Lab，非施工現場序列，此對應屬推論）",[20],"public_benchmark",[22,23,24,25],"Authors report it was the only compared method that never failed on any sequence (Sec. I; Sec. IV-C)","Highest VBR robustness score (AUC 288.57 versus 275.98 for KISS-ICP) (Table III)","Stairs sequence RPE 0.91% where KISS-ICP, MULLS and CT-ICP failed (Table II)","Information-aware map update improves accuracy over naive selection on Newer College (average 1.84% versus 1.97%) (Table I)",[27,28,29,30],"Surface normals unreliable in sparse regions, mitigated by normal propagation from flat parent nodes (Sec. III-A)","Revisits degrade to localization against existing keyframes without map update (Sec. III-C) (author-described behaviour)","Less accurate than KISS-ICP and CT-ICP on KITTI (average 0.82% versus 0.54% and 0.53%) and than KISS-ICP on MulRan (4.34% versus 3.82%) (Table II)","Averages exclude each method's failures, so averages are not computed over the same sequence set (Sec. 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