[{"data":1,"prerenderedAt":442},["ShallowReactive",2],{"method-loamlivox2020":3},{"method":4,"reference":55,"equipment":74,"figures":113,"results":114},{"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":22,"limitations":27,"sensors":31,"platform":33,"estimator":35,"association":36,"timeModel":37,"deskew":38,"loopClosure":39,"globalOptimization":40,"mapRepresentation":41,"prior":42,"outputGeometry":43,"compute":44,"codeUrl":45,"codeLicense":46,"relatedVersions":47},"loamlivox2020","Lin & Zhang, 2020","Loam_livox","Loam livox: A fast, robust, high-precision LiDAR odometry and mapping package for LiDARs of small FoV",2020,"recent","C04","odometry_with_local_mapping","Loam_livox 把 LOAM 流程改寫給小視野、非重複掃描的固態 LiDAR（Livox Mid-40）。前端依視野邊緣、回波強度、入射角與遮蔽關係剔除不可靠的點，並把反射率突變視為額外的邊緣特徵，以緩解小視野下特徵不足與退化。每一幀直接與全域特徵地圖配準，並以「分段處理」（piecewise processing，將一幀切成三個子幀分別配準）處理手持抖動造成的運動模糊。論文本身不含迴圈閉合。","Loam_livox adapts LOAM to small-FoV non-repetitive solid-state lidars through physics-based point selection, reflectivity edges, direct frame-to-map matching with outlier trimming, and piecewise sub-frame motion compensation.","full_text_reviewed","peer_reviewed_published","background","not_reported（論文展示校園樓梯、欄杆與大尺度校園建圖，但未在營建工地驗證；（推論）低成本固態 LiDAR 與手持運動模糊處理和工地手持掃描情境相關，需另行驗證）。",[20,21],"controlled_experiment","independent_reference",[23,24,25,26],"Piecewise processing removed motion blur on stairs and railings and avoided the long-term bending seen with linear interpolation in handheld data (Sec. V-A, Fig. 8)","traveled-distance comparison against GPS measurements, compared via Google maps per the Fig. 9 caption, one outdoor and one indoor dataset: 0.41% and 0.65% (Sec. V-B, Fig. 9)","average Euler-angle error about 1.1 deg against motion capture (Sec. V-B, Fig. 10)","per-frame time 35.68 ms (desktop i7-9700K) and 54.60 ms (DJI Manifold 2, i7-8550U), 17.24 ms and 32.54 ms with 3 parallel threads, versus 109.00 ms and 125.13 ms for the A-LOAM baseline, i.e., 2 to 3 times faster (Sec. V-C, Table I)",[28,29,30],"Small FoV yields few features and degeneracy, and makes matching easily disturbed by moving objects (Sec. I)","Linear interpolation cannot capture jerky handheld motion (Sec. V-A)","No loop closure or global optimization in the paper (Sec. VI)",[32],"solid-state LiDAR (Livox Mid-40, 38.4 deg circular FoV, non-repetitive scan)",[34],"handheld","iterative nonlinear least-squares pose optimization with 20% largest-residual trimming (Algorithm 1)","point selection by FoV fringe, intensity, incidence angle and occlusion; LOAM-style smoothness features plus reflectivity-change edges; edge-to-edge and plane-to-plane residuals using 5 nearest map points with eigenvalue checks (Sec. III, IV-A, IV-B)","discrete frame poses; each frame split into three sub-frames (piecewise processing) or linear interpolation (Sec. IV-C)","piecewise processing (three sub-frames matched independently) or linear pose interpolation; piecewise preferred for jerky handheld motion (Sec. IV-C, V-A)","none in this paper; a companion preprint (arXiv 1909.11811) and the repository README describe an added loop-closure module","none in this paper","global maps of edge and planar features in memory; raw points saved to disk for possible offline processing (Fig. 5 caption)","none","feature maps and 20 Hz odometry; raw points retained on disk (Fig. 5 caption)","Odometry and mapping both at 20 Hz; sub-frame matching and KD-tree building in parallel threads. Per-frame time: desktop PC i7-9700K (4.0-4.8 GHz) 35.68 ms, 17.24 ms with 3 threads; onboard DJI Manifold 2 (i7-8550U, 3.0-3.5 GHz) 54.60 ms, 32.54 ms with 3 threads (Sec. V-C, Table I)","https:\u002F\u002Fgithub.com\u002Fhku-mars\u002Floam_livox","GPL-2.0 (LICENSE file)",[48,52],{"relation":49,"title":50,"doi_or_url":51},"preprint","arXiv 1909.06700","https:\u002F\u002Farxiv.org\u002Fabs\u002F1909.06700",{"relation":53,"title":54,"doi_or_url":45},"code_release","hku-mars\u002Floam_livox",{"id":5,"kind":56,"shortName":7,"title":8,"authors":57,"year":9,"venue":60,"venueType":61,"publisher":62,"volumeIssuePages":63,"doi":64,"arxivId":65,"url":51,"firstPublicDate":66,"publicationStatus":16,"metadataStatus":67,"fulltextStatus":15,"era":10,"classicReason":68,"codeUrl":45,"cluster":11,"topics":69,"mdpi":70,"verification":71,"label":6,"fulltextRoute":72,"versionRead":73,"addedByCensus":70},"method",[58,59],"Jiarong Lin","Fu Zhang","2020 IEEE International Conference on Robotics and Automation (ICRA)","conference","IEEE","pp. 3126-3131","10.1109\u002Ficra40945.2020.9197440","1909.06700","2019-09-15","metadata_verified","not_applicable",[11],false,"confirmed","arXiv","arXiv 1909.06700 v1 (2019-09-15); IEEE ICRA 2020 version of record not read",[75,82,88,93,99,104,110],{"category":76,"model":77,"canonical":77,"role":78,"dataset":79,"specs":80,"locator":81},"lidar","Livox MID40","method input",null,"written 'Livox Mid-40' in Fig. 3; front-facing conical FoV of 38.4 deg; rosette-like non-repetitive scanning; 20 ms per frame (Fig. 4); point-selection thresholds stated for MID40 (deflection angle >= 17 deg removed, intensity limits 7e-3 and 1e-1, incidence angle limits 5 and 175 deg). Sec. V and the experiment figures do not name the LiDAR on the hand-held rig; MID40 is inferred from the method parameters","Sec. I, III-A; Figs. 3-4",{"category":83,"model":84,"canonical":84,"role":85,"dataset":79,"specs":86,"locator":87},"camera","Camera on the hand-held device (model not stated)","dataset sensor","shown on the hand-held rig; not used by the algorithm, which uses no IMU, GPS or camera","Fig. 8d; Sec. I",{"category":89,"model":90,"canonical":90,"role":78,"dataset":79,"specs":91,"locator":92},"platform","Hand-held device carrying LiDAR, camera and laptop","used for data collection; motion described as jerky","Fig. 8d; Sec. V-A",{"category":94,"model":95,"canonical":95,"role":96,"dataset":79,"specs":97,"locator":98},"other","Motion capture system (model not stated)","reference or ground truth","rotation reference; Euler angles compared","Sec. V-B, Fig. 10",{"category":100,"model":101,"canonical":101,"role":96,"dataset":79,"specs":102,"locator":103},"gnss","GPS measurement","start and end coordinates printed in Fig. 9; Sec. V-B compares odometry distance with the GPS measurement and the Fig. 9 caption says results were compared with Google maps to compute traveled distance; receiver not stated","Sec. V-B, Fig. 9",{"category":105,"model":106,"canonical":106,"role":107,"dataset":79,"specs":108,"locator":109},"compute","Desktop PC with Intel i7-9700K","compute for runtime","4.0-4.8 GHz; 3 threads in parallel mode","Sec. V-C, Table I",{"category":105,"model":111,"canonical":111,"role":107,"dataset":79,"specs":112,"locator":109},"DJI Manifold 2 onboard computer (i7-8550U)","3.0-3.5 GHz; 3 threads in parallel mode",[],{"totalRows":115,"groupCount":116,"groups":117,"others":441},9,4,[118,175,215,250],{"slug":119,"group":120,"sourceId":5,"sourceLabel":6,"table":121,"selfRows":116,"metrics":122,"seqs":127,"entrants":137,"cells":144,"outcomes":164,"locators":166,"hardware":167,"wordings":172,"notes":173},"loamlivox2020-table-i","loamlivox2020:Table I","Table I",[123],{"label":124,"unit":125,"statistic":126,"alignment":42},"time consumption per frame","ms","not_reported",[128,131,133,135],{"dataset":129,"sequence":130,"environment":126},"not_reported (data used for timing not stated)","Desktop PC @4.0-4.8 GHz",{"dataset":129,"sequence":132,"environment":126},"Desktop PC parallel",{"dataset":129,"sequence":134,"environment":126},"Onboard PC @3.0-3.5 GHz",{"dataset":129,"sequence":136,"environment":126},"Onboard PC parallel",[138,141],{"name":139,"methodId":5,"linkable":140,"proposed":140,"self":140},"Ours (Loam_livox)",true,{"name":142,"methodId":143,"linkable":140,"proposed":70,"self":70},"Baseline (A-LOAM)","aloam_software",[145,149,152,154,155,158,160,163],[146,146,146,147,148,146,146,148,146],0,35.68,-1,[150,146,146,151,148,146,146,148,146],1,109,[146,146,150,153,148,146,150,148,146],17.24,[150,146,150,79,146,146,150,148,146],[146,146,156,157,148,146,156,148,146],2,54.6,[150,146,156,159,148,146,156,148,146],125.13,[146,146,161,162,148,146,161,148,146],3,32.54,[150,146,161,79,146,146,161,148,146],[165],"not_applicable (NaN in table)",[121],[168,169,170,171],"desktop PC, Intel i7-9700K","desktop PC, Intel i7-9700K, 3 threads","DJI Manifold 2, i7-8550U","DJI Manifold 2, i7-8550U, 3 threads",[],[174],"Time consumption per frame; both methods use piecewise processing; parallel columns use 3 threads for registration",{"slug":176,"group":177,"sourceId":5,"sourceLabel":6,"table":178,"selfRows":161,"metrics":179,"seqs":187,"entrants":199,"cells":201,"outcomes":208,"locators":209,"hardware":210,"wordings":211,"notes":212},"loamlivox2020-text-sec-v-b","loamlivox2020:Text Sec. V-B","Text Sec. V-B",[180,183],{"label":181,"unit":182,"statistic":126,"alignment":42},"traveled-distance error against GPS (Google Maps) positions","%",{"label":184,"unit":185,"statistic":186,"alignment":126},"average error of Euler angles in all three directions","deg","mean",[188,192,195],{"dataset":189,"sequence":190,"environment":191},"author-collected Livox MID40 data","dataset 1 (outdoor, Fig. 9 upper)","outdoor",{"dataset":189,"sequence":193,"environment":194},"dataset 2 (indoor, Fig. 9 lower)","indoor",{"dataset":196,"sequence":197,"environment":198},"author-collected Livox MID40 data with mocap","mocap sequence (Fig. 10)","not_reported (motion capture area)",[200],{"name":7,"methodId":5,"linkable":140,"proposed":140,"self":140},[202,204,206],[146,146,146,203,148,146,148,148,146],0.41,[146,146,150,205,148,146,148,148,146],0.65,[146,150,156,207,148,150,148,148,150],1.1,[],[103,98],[],[],[213,214],"Odometry distance between two positions compared with distance from GPS coordinates on Google Maps","Rotation accuracy against a motion capture system",{"slug":216,"group":217,"sourceId":218,"sourceLabel":219,"table":220,"selfRows":150,"metrics":221,"seqs":224,"entrants":229,"cells":236,"outcomes":243,"locators":244,"hardware":245,"wordings":247,"notes":248},"fastlio2021-table-iii","fastlio2021:Table III","fastlio2021","Xu & Zhang, 2021","Table III",[222],{"label":223,"unit":125,"statistic":126,"alignment":68},"Running time",[225],{"dataset":226,"sequence":227,"environment":228},"own handheld indoor data","indoor fast shaking","indoor, handheld, angular velocity often above 100 deg\u002Fs",[230,232,234],{"name":231,"methodId":5,"linkable":140,"proposed":70,"self":140},"LOAM (livox_mapping implementation [10])",{"name":233,"methodId":79,"linkable":70,"proposed":70,"self":70},"LOAM+IMU (livox_horizon_loam, loosely coupled)",{"name":235,"methodId":218,"linkable":140,"proposed":140,"self":70},"FAST-LIO",[237,239,241],[146,146,146,238,148,146,146,148,146],59,[150,146,146,240,148,146,146,148,146],44,[156,146,146,242,148,146,146,148,146],23,[],[220],[246],"DJI Manifold 2-C (1.8 GHz quad-core Intel i7-8550U, 8 GB RAM)",[],[249],"Processing time for a LiDAR scan at 10 Hz in the handheld indoor large-rotation test; LOAM variants use FAST-LIO feature extraction; effective features: LOAM 1107, LOAM+IMU 1107, FAST-LIO 1430",{"slug":251,"group":252,"sourceId":253,"sourceLabel":254,"table":255,"selfRows":150,"metrics":256,"seqs":259,"entrants":263,"cells":356,"outcomes":424,"locators":436,"hardware":437,"wordings":438,"notes":439},"ghadimzadeh2025slamnde-table-3","ghadimzadeh2025slamnde:Table 3","ghadimzadeh2025slamnde","Ghadimzadeh Alamdari et al., 2025","Table 3",[257],{"label":258,"unit":42,"statistic":126,"alignment":42},"Result (run outcome)",[260],{"dataset":261,"sequence":126,"environment":262},"Luleå SubT tunnel dataset (Koval et al. 2022)","underground tunnel",[264,266,268,271,274,276,278,280,283,285,288,290,292,295,298,300,303,306,309,312,315,317,320,322,324,326,329,331,333,336,339,342,345,347,349,351,353],{"name":265,"methodId":143,"linkable":140,"proposed":70,"self":70},"LOAM and 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1",{"name":318,"methodId":319,"linkable":140,"proposed":70,"self":70},"Fast-LIO 2 and SC-Fast-LIO 2","fastlio2_2022",{"name":321,"methodId":79,"linkable":70,"proposed":70,"self":70},"D-LIOM",{"name":323,"methodId":79,"linkable":70,"proposed":70,"self":70},"Hand-held mobile mapping",{"name":325,"methodId":79,"linkable":70,"proposed":70,"self":70},"HectorGrapher",{"name":327,"methodId":328,"linkable":140,"proposed":70,"self":70},"Cartographer","cartographer2016",{"name":330,"methodId":79,"linkable":70,"proposed":70,"self":70},"LOCUS and LOCUS 2",{"name":332,"methodId":79,"linkable":70,"proposed":70,"self":70},"CamVox",{"name":334,"methodId":335,"linkable":140,"proposed":70,"self":70},"LVI-SAM","lvisam2021",{"name":337,"methodId":338,"linkable":140,"proposed":70,"self":70},"R2LIVE","r2live2021",{"name":340,"methodId":341,"linkable":140,"proposed":70,"self":70},"R3LIVE","r3live2022",{"name":343,"methodId":344,"linkable":140,"proposed":70,"self":70},"FAST-LIVO(s)","fastlivo2022",{"name":346,"methodId":79,"linkable":70,"proposed":70,"self":70},"LIMO",{"name":348,"methodId":79,"linkable":70,"proposed":70,"self":70},"DV-LOAM",{"name":350,"methodId":79,"linkable":70,"proposed":70,"self":70},"DVL-SLAM",{"name":352,"methodId":79,"linkable":70,"proposed":70,"self":70},"Multiverse Odometry",{"name":354,"methodId":355,"linkable":140,"proposed":70,"self":70},"Super Odometry","superodom2021",[357,358,359,360,361,362,364,366,368,370,371,373,375,377,379,381,383,385,387,389,391,393,395,397,398,400,402,404,406,408,410,412,414,416,418,420,422],[146,146,146,79,146,146,148,148,146],[150,146,146,79,150,146,148,148,146],[156,146,146,79,156,146,148,148,146],[161,146,146,79,156,146,148,148,146],[116,146,146,79,161,146,148,148,146],[363,146,146,79,156,146,148,148,146],5,[365,146,146,79,116,146,148,148,146],6,[367,146,146,79,363,146,148,148,146],7,[369,146,146,79,150,146,148,148,146],8,[115,146,146,79,161,146,148,148,146],[372,146,146,79,161,146,148,148,146],10,[374,146,146,79,365,146,148,148,146],11,[376,146,146,79,365,146,148,148,146],12,[378,146,146,79,365,146,148,148,146],13,[380,146,146,79,367,146,148,148,146],14,[382,146,146,79,150,146,148,148,146],15,[384,146,146,79,369,146,148,148,146],16,[386,146,146,79,156,146,148,148,146],17,[388,146,146,79,156,146,148,148,146],18,[390,146,146,79,150,146,148,148,146],19,[392,146,146,79,115,146,148,148,146],20,[394,146,146,79,372,146,148,148,146],21,[396,146,146,79,369,146,148,148,146],22,[242,146,146,79,363,146,148,148,146],[399,146,146,79,369,146,148,148,146],24,[401,146,146,79,365,146,148,148,146],25,[403,146,146,79,365,146,148,148,146],26,[405,146,146,79,150,146,148,148,146],27,[407,146,146,79,156,146,148,148,146],28,[409,146,146,79,150,146,148,148,146],29,[411,146,146,79,150,146,148,148,146],30,[413,146,146,79,150,146,148,148,146],31,[415,146,146,79,365,146,148,148,146],32,[417,146,146,79,369,146,148,148,146],33,[419,146,146,79,369,146,148,148,146],34,[421,146,146,79,369,146,148,148,146],35,[423,146,146,79,367,146,148,148,146],36,[425,426,427,428,429,430,431,432,433,434,435],"success (row covers LOAM and A-LOAM; A-LOAM is the variant discussed in the results)","not_run (incompatible with VLP-16)","success","failed (trajectory estimation)","not_run (incompatible with the testing dataset)","not_run (not integrated with ROS)","not_run (authors could not run the code)","not_run (no publicly available repository)","not_run (inconsistent repository)","not_run (repository no longer available)","success (row covers Fast-LIO 2 and its Scan Context variant)",[255],[],[],[440],"Run outcome ('Result' column) of each reviewed LiDAR-based and combined method on the Luleå tunnel test dataset; '*' marks incompatible with VLP-16, '+' marks not integrated with ROS; some rows combine two methods",[],1790510655174]