[{"data":1,"prerenderedAt":626},["ShallowReactive",2],{"method-slict2023":3},{"method":4,"reference":65,"equipment":89,"figures":138,"results":139},{"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":23,"limitations":29,"sensors":35,"platform":38,"estimator":42,"association":43,"timeModel":44,"deskew":45,"loopClosure":46,"globalOptimization":47,"mapRepresentation":48,"prior":49,"outputGeometry":50,"compute":51,"codeUrl":52,"codeLicense":53,"relatedVersions":54},"slict2023","Nguyen et al., 2023","SLICT","SLICT: Multi-Input Multi-Scale Surfel-Based Lidar-Inertial Continuous-Time Odometry and Mapping",2023,"recent","C05","full_slam_with_global_correction","SLICT 以 UFOMap 八元樹維護全域多尺度面元（surfel）地圖，每個節點只存點數、座標和與散佈矩陣，因此子節點新增或刪除時可遞增更新父節點面元，不必反覆重建整張地圖的 k-d 樹。前端把一顆或多顆 LiDAR 的點合併成單一串流，每個原始點依時間戳記在滑動視窗兩個相鄰狀態間內插位姿，直接與多個尺度中平面度足夠的面元建立點到面元因子，再與 IMU 預積分因子一起以 Ceres 最佳化。後端在 K 個最近關鍵影格中找出時間差足夠大的候選作為迴圈，以 ICP 計算相對位姿，做位姿圖最佳化後重算全域地圖。","Multi-LiDAR continuous-time LIO and mapping that keeps an incrementally updatable global multi-scale surfel octree (UFOMap), associates each raw point to planar surfels at several scales with time-interpolated states in a sliding-window optimization with IMU preintegration, and adds ICP-based loop closure with pose-graph optimization.","full_text_reviewed","peer_reviewed_published","supplementary","論文未在營建工地驗證；資料為 NTU VIRAL 無人機、Newer College 手持與 NTU 校園 ATV 序列。作者自建資料以 Leica RTC360 靜態掃描地圖加 ICP 配準產生參考軌跡，屬獨立幾何參考，但只評估軌跡 ATE，未評估點雲幾何精度。多 LiDAR 合併與全域面元地圖的做法，可對應工地機器人同時搭載旋轉式與固態 LiDAR 以提高覆蓋的需求（推論）。GLIM [glim2024] 在 NTU VIRAL 比較中列入 SLICT，RESPLE [resple2025] 比較其後繼版本 SLICT2。",[20,21,22],"public_benchmark","independent_reference","cross_site",[24,25,26,27,28],"Lowest ATE in 15 of the 18 NTU VIRAL sequences against MARS, LIO-SAM and FAST-LIO2; MARS and LIO-SAM diverged on some sequences (Table I)","Lowest ATE on 4 of 5 Newer College sequences (Table II)","Without loop closure, lowest ATE on all three in-house ATV sequences where MARS diverged in all and LIO-SAM in one; loop closure lowered ATE further to 0.74 to 0.98 m (Table III)","Global map lets current scans associate with the earliest features instead of only recent local-map features (Sec. I, Fig. 1)","Accepts any number of LiDARs, including spinning Ouster and prism-based Livox units, as one merged stream (Sec. I, III-A, IV-C)",[30,31,32,33,34],"Real time is not guaranteed: about 165 ms per cycle versus a 100 ms LiDAR period on a Core i7 PC (Sec. IV-A)","Association over five scales costs at least several times the single-scale direct method (Sec. IV-A)","Largest errors appear in the vertical direction on sites with elevation change (Sec. IV-B, IV-C, Figs. 7 and 11)","Point-to-surfel association uses simple predicates and loop closure is basic (Sec. V)","FAST-LIO2 had lower ATE on NTU VIRAL spms_02 and spms_03 and Newer College 06, and LIO-SAM on tnp_03 (Tables I and II)",[36,37],"one or several 3D LiDARs merged into one stream (Ouster OS1-128 plus prism-based Livox Mid-70 in-house; horizontal and vertical LiDARs in NTU VIRAL; Ouster 64-channel in Newer College)","IMU (VectorNav VN100 in-house; built-in 100 Hz Ouster IMU in Newer College)",[39,40,41],"UAV (NTU VIRAL)","handheld (Newer College)","ATV (all-terrain vehicle, in-house, up to 30 km\u002Fh)","sliding-window MAP optimization in Ceres over several states per LiDAR sweep (2 to 8 new states per cloud; 400 ms window with 16 intervals in NTU VIRAL), with IMU preintegration factors and continuous-time point-to-surfel factors; the deskew, association and optimization steps can be iterated (Sec. II-C, III-A, III-F)","multi-scale point-to-surfel: each point is matched to every surfel at octree depths 1 to Dmax whose voxel intersects a sphere around the point, with enough points and planarity above a threshold, and whose plane distance is below dmax; five scales from 2 to 32 times the 0.1 m leaf size were used (Sec. III-D, IV-A)","continuous-time in the sense of per-point factors: each raw point is coupled to the two bounding sliding-window states by linear interpolation of position and SO(3) interpolation of rotation at its normalized timestamp (Sec. II-C, III-D)","IMU-propagated poses interpolated with slerp per point for association; the optimization itself uses raw points with time-interpolated states (Sec. III-C, III-D)","yes; proximity-based candidate among K nearest keyframes with sufficient time difference, relative pose by ICP (Sec. III-I)","pose-graph optimization over keyframes with relative-pose priors and loop constraints, followed by recomputing the global map (Eq. 8, Sec. III-I)","global multi-scale surfel map in an octree built on UFOMap; each node stores point count, sum and scatter so surfels can be added or removed incrementally with Welford-type updates (Sec. II-D)","none (LiDAR extrinsics for merging multiple LiDARs assumed known; not described)","trajectory, keyframe poses with deskewed keyframe clouds, and a global point and surfel map (Figs. 8 and 12)","not guaranteed real time: about 165 ms per cycle on NTU VIRAL nya_02 (about 50 ms solving) on a Core i7 PC, versus 100 ms LiDAR period; authors expect real time with more CPU threads (Sec. IV-A)","https:\u002F\u002Fgithub.com\u002Fbrytsknguyen\u002Fslict","GPL-2.0 (LICENSE file checked)",[55,59,62],{"relation":56,"title":57,"doi_or_url":58},"preprint","arXiv 2211.03900 (v1 2022-11-07, v2 2022-11-09)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2211.03900",{"relation":56,"title":60,"doi_or_url":61},"DR-NTU repository copy (submitted version, not opened)","https:\u002F\u002Fhdl.handle.net\u002F10356\u002F228819",{"relation":63,"title":64,"doi_or_url":52},"code_release","brytsknguyen\u002Fslict (code and in-house datasets)",{"id":5,"kind":66,"shortName":7,"title":8,"authors":67,"year":9,"venue":73,"venueType":74,"publisher":75,"volumeIssuePages":76,"doi":77,"arxivId":78,"url":79,"firstPublicDate":80,"publicationStatus":16,"metadataStatus":81,"fulltextStatus":15,"era":10,"classicReason":82,"codeUrl":52,"cluster":11,"topics":83,"mdpi":84,"verification":85,"label":6,"fulltextRoute":86,"versionRead":87,"addedByCensus":88},"method",[68,69,70,71,72],"Thien-Minh Nguyen","Daniel Duberg","Patric Jensfelt","Shenghai Yuan","Lihua Xie","IEEE Robotics and Automation Letters","journal","IEEE","8(4):2102-2109","10.1109\u002Flra.2023.3246390","2211.03900","https:\u002F\u002Fdoi.org\u002F10.1109\u002FLRA.2023.3246390","2022-11-07","metadata_verified","not_applicable",[11],false,"corrected","arXiv","arXiv v2 (2022-11-09); RA-L version of record not read",true,[90,97,102,106,110,115,121,124,129,133],{"category":91,"model":92,"canonical":92,"role":93,"dataset":94,"specs":95,"locator":96},"lidar","Ouster OS1-128","method input","SLICT in-house NTU campus dataset","spinning LiDAR, merged with the Livox stream","Sec. IV-C; Sec. I",{"category":91,"model":98,"canonical":99,"role":93,"dataset":94,"specs":100,"locator":101},"Livox Mid-70","Livox MID70","Livox LiDAR (the paper describes Livox as prism-based, box-shaped, Sec. I); merged with the Ouster stream","Sec. IV-C",{"category":103,"model":104,"canonical":104,"role":93,"dataset":94,"specs":105,"locator":101},"imu","VectorNav VN100","not_reported",{"category":107,"model":108,"canonical":108,"role":93,"dataset":94,"specs":109,"locator":101},"platform","All-Terrain-Vehicle (ATV)","about 1.5 km loop, up to 30 km\u002Fh",{"category":111,"model":112,"canonical":112,"role":113,"dataset":94,"specs":114,"locator":101},"tls_scanner","Leica RTC360","reference or ground truth","survey scanner used to build a static map with centimetre accuracy; Ouster scans registered to it for ground-truth poses",{"category":91,"model":116,"canonical":116,"role":117,"dataset":118,"specs":119,"locator":120},"Ouster 64-channel LiDAR (model not stated)","dataset sensor","Newer College Dataset","64 channels, 90-degree vertical field of view","Sec. IV-B",{"category":103,"model":122,"canonical":122,"role":117,"dataset":118,"specs":123,"locator":120},"Ouster built-in IMU","100 Hz",{"category":91,"model":125,"canonical":125,"role":117,"dataset":126,"specs":127,"locator":128},"NTU VIRAL horizontal and vertical LiDARs (models not stated)","NTU VIRAL","two LiDARs merged as input","Sec. IV-A",{"category":130,"model":131,"canonical":131,"role":113,"dataset":126,"specs":132,"locator":128},"total_station","laser-tracker total station (model not stated)","centimetre-level ground truth",{"category":134,"model":135,"canonical":135,"role":136,"dataset":137,"specs":105,"locator":128},"compute","Core i7 PC (model not stated)","compute for runtime",null,[],{"totalRows":140,"groupCount":141,"groups":142,"others":620},36,5,[143,362,423,487],{"slug":144,"group":145,"sourceId":5,"sourceLabel":6,"table":146,"selfRows":147,"metrics":148,"seqs":152,"entrants":190,"cells":200,"outcomes":355,"locators":357,"hardware":358,"wordings":359,"notes":360},"slict2023-table-i","slict2023:Table I","Table I",18,[149],{"label":150,"unit":151,"statistic":105,"alignment":105},"ATE","m",[153,156,158,160,162,164,166,168,170,172,174,176,178,180,182,184,186,188],{"dataset":126,"sequence":154,"environment":155},"eee_01","UAV, indoor and outdoor spaces within a 50 m radius (Sec. IV-A)",{"dataset":126,"sequence":157,"environment":155},"eee_02",{"dataset":126,"sequence":159,"environment":155},"eee_03",{"dataset":126,"sequence":161,"environment":155},"nya_01",{"dataset":126,"sequence":163,"environment":155},"nya_02",{"dataset":126,"sequence":165,"environment":155},"nya_03",{"dataset":126,"sequence":167,"environment":155},"sbs_01",{"dataset":126,"sequence":169,"environment":155},"sbs_02",{"dataset":126,"sequence":171,"environment":155},"sbs_03",{"dataset":126,"sequence":173,"environment":155},"rtp_01",{"dataset":126,"sequence":175,"environment":155},"rtp_02",{"dataset":126,"sequence":177,"environment":155},"rtp_03",{"dataset":126,"sequence":179,"environment":155},"tnp_01",{"dataset":126,"sequence":181,"environment":155},"tnp_02",{"dataset":126,"sequence":183,"environment":155},"tnp_03",{"dataset":126,"sequence":185,"environment":155},"spms_01",{"dataset":126,"sequence":187,"environment":155},"spms_02",{"dataset":126,"sequence":189,"environment":155},"spms_03",[191,193,196,199],{"name":192,"methodId":137,"linkable":84,"proposed":84,"self":84},"MARS",{"name":194,"methodId":195,"linkable":88,"proposed":84,"self":84},"LIO-SAM","liosam2020",{"name":197,"methodId":198,"linkable":88,"proposed":84,"self":84},"FAST-LIO2","fastlio2_2022",{"name":7,"methodId":5,"linkable":88,"proposed":88,"self":88},[201,205,208,211,214,217,219,222,225,228,230,233,236,239,242,245,247,249,252,253,255,257,259,260,262,264,266,268,270,272,274,275,277,279,281,283,285,287,289,291,293,295,297,299,301,303,304,306,308,310,312,313,315,317,319,321,323,325,327,329,331,333,335,337,339,341,343,345,347,349,351,353],[202,202,202,203,204,202,204,204,202],0,0.2471,-1,[202,202,206,207,204,202,204,204,202],1,0.1033,[202,202,209,210,204,202,204,204,202],2,0.0927,[202,202,212,213,204,202,204,204,202],3,0.0555,[202,202,215,216,204,202,204,204,202],4,0.0624,[202,202,141,218,204,202,204,204,202],0.0831,[202,202,220,221,204,202,204,204,202],6,0.137,[202,202,223,224,204,202,204,204,202],7,0.1256,[202,202,226,227,204,202,204,204,202],8,0.1588,[202,202,229,137,202,202,204,204,202],9,[202,202,231,232,204,202,204,204,202],10,0.2329,[202,202,234,235,204,202,204,204,202],11,0.1377,[202,202,237,238,204,202,204,204,202],12,0.0734,[202,202,240,241,204,202,204,204,202],13,0.0681,[202,202,243,244,204,202,204,204,202],14,0.0665,[202,202,246,137,202,202,204,204,202],15,[202,202,248,137,202,202,204,204,202],16,[202,202,250,251,204,202,204,204,202],17,19.865,[206,202,202,216,204,202,204,204,202],[206,202,206,254,204,202,204,204,202],0.0457,[206,202,209,256,204,202,204,204,202],0.0403,[206,202,212,258,204,202,204,204,202],2.096,[206,202,215,137,202,202,204,204,202],[206,202,141,261,204,202,204,204,202],0.0468,[206,202,220,263,204,202,204,204,202],0.0444,[206,202,223,265,204,202,204,204,202],0.0461,[206,202,226,267,204,202,204,204,202],0.0494,[206,202,229,269,204,202,204,204,202],0.2571,[206,202,231,271,204,202,204,204,202],0.1091,[206,202,234,273,204,202,204,204,202],0.0576,[206,202,237,137,202,202,204,204,202],[206,202,240,276,204,202,204,204,202],0.033,[206,202,243,278,204,202,204,204,202],0.0283,[206,202,246,280,204,202,204,204,202],0.162,[206,202,248,282,204,202,204,204,202],0.6641,[206,202,250,284,204,202,204,204,202],1.0071,[209,202,202,286,204,202,204,204,202],0.0585,[209,202,206,288,204,202,204,204,202],0.0318,[209,202,209,290,204,202,204,204,202],0.0351,[209,202,212,292,204,202,204,204,202],0.0305,[209,202,215,294,204,202,204,204,202],0.0286,[209,202,141,296,204,202,204,204,202],0.0315,[209,202,220,298,204,202,204,204,202],0.0324,[209,202,223,300,204,202,204,204,202],0.0322,[209,202,226,302,204,202,204,204,202],0.0428,[209,202,229,267,204,202,204,204,202],[209,202,231,305,204,202,204,204,202],0.1151,[209,202,234,307,204,202,204,204,202],0.0543,[209,202,237,309,204,202,204,204,202],0.0432,[209,202,240,311,204,202,204,204,202],0.059,[209,202,243,261,204,202,204,204,202],[209,202,246,314,204,202,204,204,202],0.0686,[209,202,248,316,204,202,204,204,202],0.0821,[209,202,250,318,204,202,204,204,202],0.0603,[212,202,202,320,204,202,204,204,202],0.0316,[212,202,206,322,204,202,204,204,202],0.0249,[212,202,209,324,204,202,204,204,202],0.0275,[212,202,212,326,204,202,204,204,202],0.0229,[212,202,215,328,204,202,204,204,202],0.0227,[212,202,141,330,204,202,204,204,202],0.026,[212,202,220,332,204,202,204,204,202],0.0298,[212,202,223,334,204,202,204,204,202],0.0291,[212,202,226,336,204,202,204,204,202],0.0335,[212,202,229,338,204,202,204,204,202],0.0447,[212,202,231,340,204,202,204,204,202],0.0466,[212,202,234,342,204,202,204,204,202],0.0501,[212,202,237,344,204,202,204,204,202],0.0287,[212,202,240,346,204,202,204,204,202],0.0201,[212,202,243,348,204,202,204,204,202],0.0383,[212,202,246,350,204,202,204,204,202],0.061,[212,202,248,352,204,202,204,204,202],0.1,[212,202,250,354,204,202,204,204,202],0.0661,[356],"diverged",[146],[],[],[361],"NTU VIRAL; horizontal and vertical LiDARs merged as input for all methods; SLICT loop closure disabled; 400 ms window with 16 intervals; x = divergence",{"slug":363,"group":364,"sourceId":5,"sourceLabel":6,"table":365,"selfRows":220,"metrics":366,"seqs":368,"entrants":376,"cells":385,"outcomes":417,"locators":418,"hardware":419,"wordings":420,"notes":421},"slict2023-table-iii","slict2023:Table III","Table III",[367],{"label":150,"unit":151,"statistic":105,"alignment":105},[369,372,374],{"dataset":94,"sequence":370,"environment":371},"seq 01","campus roads with dense vegetation and up to 15 m elevation change, ATV up to 30 km\u002Fh",{"dataset":94,"sequence":373,"environment":371},"seq 02",{"dataset":94,"sequence":375,"environment":371},"seq 03",[377,378,379,380,381,383],{"name":192,"methodId":137,"linkable":84,"proposed":84,"self":84},{"name":194,"methodId":195,"linkable":88,"proposed":84,"self":84},{"name":197,"methodId":198,"linkable":88,"proposed":84,"self":84},{"name":7,"methodId":5,"linkable":88,"proposed":88,"self":88},{"name":382,"methodId":195,"linkable":88,"proposed":84,"self":84},"LIO-SAM (LC)",{"name":384,"methodId":5,"linkable":88,"proposed":88,"self":88},"SLICT (LC)",[386,387,388,389,391,393,394,396,398,400,402,404,406,408,410,411,413,415],[202,202,202,137,202,202,204,204,202],[202,202,206,137,202,202,204,204,202],[202,202,209,137,202,202,204,204,202],[206,202,202,390,204,202,204,204,202],4.0784,[206,202,206,392,204,202,204,204,202],3.7546,[206,202,209,137,202,202,204,204,202],[209,202,202,395,204,202,204,204,202],2.008,[209,202,206,397,204,202,204,204,202],1.3623,[209,202,209,399,204,202,204,204,202],1.0926,[212,202,202,401,204,202,204,204,202],1.6929,[212,202,206,403,204,202,204,204,202],1.0947,[212,202,209,405,204,202,204,204,202],0.873,[215,202,202,407,204,202,204,204,202],1.2931,[215,202,206,409,204,202,204,204,202],0.9685,[215,202,209,137,202,202,204,204,202],[141,202,202,412,204,202,204,204,202],0.9815,[141,202,206,414,204,202,204,204,202],0.7411,[141,202,209,416,204,202,204,204,202],0.8577,[356],[365],[],[],[422],"In-house NTU campus ATV dataset (Ouster OS1-128 + Livox Mid-70 merged, VN100 IMU), ground truth by registering scans to a Leica RTC360 map; LC = loop closure and pose-graph optimization; x = divergence",{"slug":424,"group":425,"sourceId":5,"sourceLabel":6,"table":426,"selfRows":141,"metrics":427,"seqs":429,"entrants":441,"cells":446,"outcomes":480,"locators":482,"hardware":483,"wordings":484,"notes":485},"slict2023-table-ii","slict2023:Table II","Table II",[428],{"label":150,"unit":151,"statistic":105,"alignment":105},[430,433,435,437,439],{"dataset":118,"sequence":431,"environment":432},"01 short experiment","handheld, college quads and park (about 200 m by 100 m)",{"dataset":118,"sequence":434,"environment":432},"02 long experiment",{"dataset":118,"sequence":436,"environment":432},"05 quad with dynamics",{"dataset":118,"sequence":438,"environment":432},"06 dynamic spinning",{"dataset":118,"sequence":440,"environment":432},"07 parkland mound",[442,443,444,445],{"name":192,"methodId":137,"linkable":84,"proposed":84,"self":84},{"name":194,"methodId":195,"linkable":88,"proposed":84,"self":84},{"name":197,"methodId":198,"linkable":88,"proposed":84,"self":84},{"name":7,"methodId":5,"linkable":88,"proposed":88,"self":88},[447,449,451,453,454,455,456,457,458,459,460,462,464,466,468,470,472,474,476,478],[202,202,202,448,204,202,204,204,202],2.1521,[202,202,206,450,204,202,204,204,202],6.003,[202,202,209,452,204,202,204,204,202],0.3729,[202,202,212,137,202,202,204,204,202],[202,202,215,137,202,202,204,204,202],[206,202,202,137,206,202,204,204,202],[206,202,206,137,206,202,204,204,202],[206,202,209,137,206,202,204,204,202],[206,202,212,137,206,202,204,204,202],[206,202,215,137,206,202,204,204,202],[209,202,202,461,204,202,204,204,202],0.3883,[209,202,206,463,204,202,204,204,202],0.3659,[209,202,209,465,204,202,204,204,202],0.3443,[209,202,212,467,204,202,204,204,202],0.08,[209,202,215,469,204,202,204,204,202],0.1356,[212,202,202,471,204,202,204,204,202],0.3843,[212,202,206,473,204,202,204,204,202],0.3496,[212,202,209,475,204,202,204,204,202],0.1155,[212,202,212,477,204,202,204,204,202],0.0844,[212,202,215,479,204,202,204,204,202],0.129,[356,481],"not_run",[426],[],[],[486],"Newer College (Ouster 64-channel, built-in 100 Hz IMU); 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RMS ATE after Umeyama alignment; star = explicit loop closures, dagger = results from DLIOM [71], double dagger = uses camera; other baselines as originally published",[621],{"group":622,"slug":623,"sourceLabel":6,"table":624,"selfRows":209,"datasets":625},"slict2023:Text Sec.IV-A","slict2023-text-sec-iv-a","Text Sec.IV-A",[126],1790510660403]