[{"data":1,"prerenderedAt":144},["ShallowReactive",2],{"devices-pipelines":3},{"families":4,"methods":34,"figures":95},[5,12,19,25,29],{"cluster":6,"cards":7,"site":8,"loop":9,"sparseOnly":10,"label":11},"C04",34,0,18,1,"C04 LOAM 系列與特徵式 LiDAR 建圖",{"cluster":13,"cards":14,"site":15,"loop":16,"sparseOnly":17,"label":18},"C05",36,4,11,2,"C05 當代 LiDAR（慣性）里程計與 SLAM",{"cluster":20,"cards":21,"site":8,"loop":22,"sparseOnly":23,"label":24},"C08",49,26,9,"C08 視覺、視覺慣性與 RGB-D 稠密 SLAM",{"cluster":26,"cards":27,"site":10,"loop":8,"sparseOnly":8,"label":28},"C12",20,"C12 地圖表示與表面重建",{"cluster":30,"cards":31,"site":10,"loop":32,"sparseOnly":17,"label":33},"C09",47,13,"C09 學習式、神經隱式與高斯潑濺 SLAM",[35,46,53,59,65,71,77,83,89],{"id":36,"name":37,"year":38,"output":39,"environments":40,"site":45},"loam2014","LOAM","2014","motion-corrected registered point cloud map (5 cm voxel-grid downsampled) and 6-DoF pose at about 10 Hz (Sec. VI); dense raw-point export not described in paper",[41,42,43,44],"公開基準","受控實驗","已完工建築","獨立參考量測",false,{"id":47,"name":48,"year":49,"output":50,"environments":51,"site":45},"legoloam2018","LeGO-LOAM","2018","feature-set point cloud map and 6-DoF poses (Sec. III-E); export of full-resolution map not described in paper",[41,52],"跨場域",{"id":54,"name":55,"year":56,"output":57,"environments":58,"site":45},"liosam2020","LIO-SAM","2020","global feature map assembled from keyframe edge and planar feature clouds plus the keyframe trajectory; lidar frames between keyframes (1 m or 10 deg pose change) are discarded; maps are shown aligned with Google Earth imagery (Figs. 4 to 7); dense map export…",[52,44],{"id":60,"name":61,"year":62,"output":63,"environments":64,"site":45},"fastlio2_2022","FAST-LIO2","2022","odometry and registered dense point map inserted at odometry rate; export format 原文未報告 in paper",[41,42],{"id":66,"name":67,"year":68,"output":69,"environments":70,"site":45},"orbslam3_2021","ORB-SLAM3","2021","Keyframe trajectories and sparse map points (on EuRoC V202 the four ORB-SLAM3 configurations keep 9,686 to 14,245 map points and 135 to 332 keyframes, Table VI); no dense reconstruction is produced or evaluated in the paper",[41],{"id":72,"name":73,"year":74,"output":75,"environments":76,"site":45},"orbslam2_2017","ORB-SLAM2","2017","keyframe trajectory and sparse map points; the dense point clouds shown are obtained by back-projecting sensor depth maps from estimated keyframe poses (Sec. IV-C, Fig. 7)",[41],{"id":78,"name":79,"year":80,"output":81,"environments":82,"site":45},"kinectfusion2011","KinectFusion","2011","dense TSDF surface rendered by raycasting; mesh export not reported in sections read",[42],{"id":84,"name":85,"year":86,"output":87,"environments":88,"site":45},"curless1996volumetric","Volumetric range-image integration (TSDF origin; VRIP)","1996","watertight triangle mesh extracted as the zero isosurface; hole filling by tessellating between empty and unseen regions",[42],{"id":90,"name":91,"year":92,"output":93,"environments":94,"site":45},"rgbdmapping2012","RGB-D Mapping (Henry et al.)","2012","globally optimised camera trajectory, dense coloured point cloud map and a surfel map (for example 28 million points combined into 1.4 million surfels, Sec. 4.4); export format 原文未報告",[42,44],[96,111,124,135],{"refId":97,"refLabel":98,"fig":99,"whatZh":100,"license":101,"licenseUrl":102,"sourceUrl":103,"src":104,"width":105,"height":106,"thumb":107,"thumbWidth":108,"thumbHeight":109,"modified":110},"lee2024lidarodom_survey","Lee et al., 2024b","Fig. 3","LiDAR 里程計通用流程：前處理（感測器同步、運動補償、降取樣、特徵擷取）、初始估計與狀態估計，並標示其他感測器以鬆耦合或緊耦合方式接入的位置","CC BY 4.0 (Springer open access, licence statement in the article)","https:\u002F\u002Fcreativecommons.org\u002Flicenses\u002Fby\u002F4.0\u002F","https:\u002F\u002Fmedia.springernature.com\u002Flw1200\u002Fspringer-static\u002Fimage\u002Fart%3A10.1007%2Fs11370-024-00515-8\u002FMediaObjects\u002F11370_2024_515_Fig3_HTML.png","\u002Ffigure-files\u002Flee2024lidarodom_survey\u002Ffig-3.webp",1200,1291,"\u002Ffigure-files\u002Flee2024lidarodom_survey\u002Ffig-3.thumb.webp",480,516,"converted to WebP",{"refId":112,"refLabel":113,"fig":114,"whatZh":115,"license":116,"licenseUrl":117,"sourceUrl":118,"src":119,"width":120,"height":121,"thumb":122,"thumbWidth":108,"thumbHeight":123,"modified":110},"li2026slamgenerationaec","Li et al., 2026a","Fig. 6","SLAM 生成流程：感測輸入、特徵擷取、狀態估計、後端最佳化與地圖建構","CC BY 4.0","http:\u002F\u002Fcreativecommons.org\u002Flicenses\u002Fby\u002F4.0\u002F","https:\u002F\u002Fars.els-cdn.com\u002Fcontent\u002Fimage\u002F1-s2.0-S1226798825005239-gr6.jpg","\u002Ffigure-files\u002Fli2026slamgenerationaec\u002Ffig-6.webp",508,158,"\u002Ffigure-files\u002Fli2026slamgenerationaec\u002Ffig-6.thumb.webp",149,{"refId":125,"refLabel":126,"fig":127,"whatZh":128,"license":116,"licenseUrl":102,"sourceUrl":129,"src":130,"width":131,"height":132,"thumb":133,"thumbWidth":108,"thumbHeight":134,"modified":110},"vizzo2022vdbfusion","Vizzo et al., 2022","Figure 3","系統總覽：輸入點雲與位姿，整合為稀疏 TSDF 後輸出網格或 VDB 資料","https:\u002F\u002Fcdn.ncbi.nlm.nih.gov\u002Fpmc\u002Fblobs\u002F99b2\u002F8838740\u002F77a922d80710\u002Fsensors-22-01296-g003.jpg","\u002Ffigure-files\u002Fvizzo2022vdbfusion\u002Ffigure-3.webp",714,266,"\u002Ffigure-files\u002Fvizzo2022vdbfusion\u002Ffigure-3.thumb.webp",179,{"refId":136,"refLabel":137,"fig":138,"whatZh":139,"license":116,"licenseUrl":117,"sourceUrl":140,"src":141,"width":105,"height":142,"thumb":143,"thumbWidth":108,"thumbHeight":109,"modified":110},"feng2025_construction_lidar_eval","Feng et al., 2025","Fig. 16","十種 SLAM 演算法在實際工地建出的點雲地圖，每種方法各有前視、側視、俯視與等角視圖","https:\u002F\u002Fmedia.springernature.com\u002Flw1200\u002Fspringer-static\u002Fimage\u002Fart%3A10.1007%2Fs44242-025-00092-8\u002FMediaObjects\u002F44242_2025_92_Fig16_HTML.png","\u002Ffigure-files\u002Ffeng2025_construction_lidar_eval\u002Ffig-16.webp",1289,"\u002Ffigure-files\u002Ffeng2025_construction_lidar_eval\u002Ffig-16.thumb.webp",1790510650041]