[{"data":1,"prerenderedAt":557},["ShallowReactive",2],{"method-zebedee2012":3},{"method":4,"reference":62,"equipment":82,"figures":136,"results":137},{"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":24,"limitations":32,"sensors":42,"platform":45,"estimator":49,"association":50,"timeModel":51,"deskew":52,"loopClosure":53,"globalOptimization":54,"mapRepresentation":55,"prior":56,"outputGeometry":57,"compute":58,"codeUrl":59,"codeLicense":60,"relatedVersions":61},"zebedee2012","Bosse et al., 2012","Zebedee","Zebedee: Design of a Spring-Mounted 3-D Range Sensor with Application to Mobile Mapping",2012,"classic","C04","full_slam_with_global_correction","Zebedee 把 Hokuyo UTM-30LX 2D 雷射掃描儀與 MicroStrain 3DM-GX2 IMU 裝在同一感測頭，再以彈簧連接手把或載具，利用手持晃動或載具振動讓掃描面不規則擺動而取得三維覆蓋。配套 SLAM 改自作者先前的旋轉 2D 雷射方法：在滑動時間視窗內，以多解析度體素中時空相近的點群建立面元，在位置與法向量構成的 6 維空間做互為最近鄰的配對，並以面元匹配誤差、IMU 角速度與加速度偏差及視窗銜接條件，求解以固定間隔取樣、其間內插的連續時間軌跡修正量，同時估計雷射與 IMU 的時間延遲及 IMU 偏差；另保留少量「固定視角」面元以抑制漂移。開迴路結果再作為初值，對整段軌跡做一次批次全域配準，得到閉迴路軌跡與點雲。摘要所稱「即時」是指處理時間短於資料擷取時間，論文實驗實際是在收集後離線處理。","Spring-mounted 2D lidar (Hokuyo UTM-30LX) plus IMU (MicroStrain 3DM-GX2) sensor with sliding-window continuous-time surfel SLAM that also estimates laser-IMU latency and IMU biases, limits drift with fixed views, and refines the whole trajectory by batch global registration; processing ran faster than acquisition but offline in the experiments.","full_text_reviewed","peer_reviewed_published","background","論文動機提到測量、行動建圖與受限空間中的基礎設施檢測，並表示已部署於大型組裝廠房與室內空間，但未提供這些部署的量化結果（Sec. I、III、V）；實驗場景為辦公室、走廊、三層樓梯間、戶外中庭、高草地與道路，未在營建工地驗證。（推論）輕量手持與背負式配置適合工地巡檢式掃描，但須確保雷射與 IMU 計時同步及足夠的感測頭擺動。",[20,21,22,23],"controlled_experiment","independent_reference","completed_building","cross_site",[25,26,27,28,29,30,31],"Closed-loop positional RMS error vs Vicon of 0.88, 0.72 and 0.69 cm with 0, 2 and 5 fixed views (Sec. IV-C, Fig. 12)","Mobile mapping point-cloud error std of 3.9 cm (office) and 4.1 cm (courtyard) relative to a spinning SICK LMS291 reference cloud (Sec. IV-B)","Pseudostationary error std of 2 to 2.5 cm in hallway and stairwell and around 5 cm in the remaining datasets; the authors state that the manufacturer's 3 to 5 cm range accuracy agrees with the minimum error std among the environments (Sec. IV-A)","Fixed views cut open-loop drift from about 5 to 10 cm\u002Fmin to about 1.5 and 0.5 cm\u002Fmin (2 and 5 fixed views) and keep the solution robust to oscillation stoppages of several seconds (Sec. IV-C, IV-D)","Both open-loop and global steps ran in less than the data acquisition time (Sec. IV-B)","Light and mechanically simple: total mass well under 0.5 kg, hardware cost essentially a 2D laser plus an IMU (Sec. I, II)","Hands-free backpack mounting gives enough excitation for reliable trajectory estimation (Sec. IV-D)",[33,34,35,36,37,38,39,40,41],"Degenerate environments such as long smooth tunnel-like spaces or large featureless open areas, and scenes dominated by moving objects (Sec. V-A)","Needs fairly continual excitation of the sensor head; may not suit electric ground vehicles on smooth terrain (Sec. V-A)","The estimated trajectory is that of the oscillating sensor head, not the carrying platform (Sec. V-A)","No uncertainty (covariance) estimate (Sec. V-A)","Very sensitive to laser-IMU timing: about 10 cm point error at 10 m per millisecond of latency error at 600 deg\u002Fs, with latency drift of about 1 ms\u002Fmin observed (Sec. II-B, IV-B)","Global registration needs a good initial guess; with zero fixed views a 6 s oscillation stoppage on stairs produced about 5 m vertical error (Sec. III-F, IV-D)","Range bias of about 1 to 2 cm toward the sensor, attributed to the Hokuyo laser (Sec. IV-A)","Vicon orientation precision (about 1 deg) allowed only position errors to be evaluated, within about a 2 x 2 m region (Sec. IV-C)","(inference) Stairwell and backpack accuracy were measured against the method's own closed-loop solutions rather than an independent reference (Sec. IV-D)",[43,44],"2D time-of-flight laser Hokuyo UTM-30LX (270 deg FoV, 30 m maximum range, 40 Hz) (Sec. II)","Industrial-grade MEMS IMU MicroStrain 3DM-GX2 at 100 Hz with a rotational rate range of at least 600 deg\u002Fs; the second-generation device uses a MicroStrain 3DM-GX3 (Sec. II, IV-D)",[46,47,48],"handheld (first generation tethered to a pushcart for power and logging; later with electronics in a backpack) (Sec. IV, Fig. 2)","hands-free backpack-mounted configuration (Sec. IV-D, Fig. 2(e))","dual-spring vehicle mount on a John Deere Gator TE electric vehicle, used for spring characterization only; no vehicle SLAM results are reported (Sec. II-A, Fig. 1)","Sliding-window continuous-time trajectory correction: stacked 6-DoF corrections sampled at regular intervals (linear interpolation assumed for the Jacobians), linearized and solved as Ax=b by iteratively reweighted least squares with a Lorentzian M-estimator and a decreasing outlier threshold; terms are surfel match errors, IMU acceleration and rotational-rate deviations and initial-condition constraints; the state is augmented with laser-IMU latency and IMU bias corrections (Sec. III-B to III-D)","Surfels from spatially and temporally proximal point clusters in a multiresolution voxel grid (resolution doubling per level, two grids offset by half a cell), planarity-filtered; clusters whose rotational velocity normal to the scan plane is below about 15 deg\u002Fs are discarded; approximate kNN in the 6-D position and normal space via a kd-tree, reciprocal matches only, with time separation above half the nominal sweep period; correspondences recomputed every iteration (Sec. III-A)","continuous-time: trajectory corrections sampled at regular intervals and interpolated in between; each window advances by a fraction of its length and the first three correction samples are constrained for continuity (Sec. III, III-B)","implicit: laser points are projected with the continuous-time trajectory estimate, and laser-IMU latency is estimated online in each window (Sec. III, III-C)","no explicit loop detection or place recognition; loops are closed implicitly by batch global registration of surfel correspondences over the whole trajectory, which needs a good open-loop initial guess (Sec. III-F)","batch global registration over the entire trajectory in one window, minimizing surfel match errors, deviations from the open-loop velocities and gravity deviations; latency and IMU biases are kept from the open-loop solution (Sec. III-F)","view-based: the trajectory is the full state and raw points are projected when needed; multiresolution surfels for matching plus a small buffer of fixed views (surfels from recent finalized windows) (Sec. III, III-A, III-E)","none","6-DoF sensor-head trajectory and a 3D point cloud projected with the closed-loop trajectory (Sec. III-F, IV-B; Figs. 7, 8, 16)","MATLAB with C++ MEX on a 3.2 GHz Intel Xeon CPU; open-loop processing took about 62 %, 71 % and 73 % of acquisition time with 0, 2 and 5 fixed views; global optimization took under 1 min (3.5 min office) and under 2 min (6.5 min courtyard); experiments were processed offline after collection, and a real-time C++ version was under development (Sec. IV-B, V-B)",null,"not_verified",[],{"id":5,"kind":63,"shortName":7,"title":8,"authors":64,"year":9,"venue":68,"venueType":69,"publisher":70,"volumeIssuePages":71,"doi":72,"arxivId":59,"url":73,"firstPublicDate":74,"publicationStatus":16,"metadataStatus":75,"fulltextStatus":15,"era":10,"classicReason":76,"codeUrl":59,"cluster":11,"topics":77,"mdpi":78,"verification":79,"label":6,"fulltextRoute":80,"versionRead":81,"addedByCensus":78},"method",[65,66,67],"M. Bosse","R. Zlot","P. Flick","IEEE Transactions on Robotics","journal","IEEE","28(5): 1104-1119","10.1109\u002Ftro.2012.2200990","https:\u002F\u002Fapi.crossref.org\u002Fworks\u002F10.1109\u002FTRO.2012.2200990","2012-06-19","metadata_verified","principle reused: Wildcat describes its odometry as an online implementation of concepts from this paper and from Bosse and Zlot 2009 (wildcat2022 Sec. I-II), and Elastic LiDAR Fusion benchmarks its map deformation against this paper's batch continuous-time SLAM trajectory (elasticlidarfusion2018 Sec. VII-B, ref. [3]); needed to explain the handheld continuous-time lineage.",[11],false,"corrected","NTU institutional (Chrome)","IEEE version of record, IEEE Transactions on Robotics 28(5):1104-1119 (published 2012-06-19), IEEE Xplore full-text HTML",[83,89,94,99,104,108,113,118,122,127,130],{"category":84,"model":85,"canonical":85,"role":86,"dataset":59,"specs":87,"locator":88},"lidar","Hokuyo UTM-30LX","method input","2D time-of-flight, 270 deg FoV, 30 m maximum range, 40 Hz scan rate, 60 x 60 x 85 mm, 210 g; manufacturer range accuracy 3 to 5 cm as cited by the authors","Sec. II; Sec. IV-A",{"category":90,"model":91,"canonical":91,"role":86,"dataset":59,"specs":92,"locator":93},"imu","MicroStrain 3DM-GX2","industrial-grade, triaxial MEMS gyros and accelerometers, 100 Hz output, rotational rate range of at least 600 deg\u002Fs (nonstandard option), 41 x 63 x 32 mm, 50 g","Sec. II",{"category":90,"model":95,"canonical":96,"role":86,"dataset":59,"specs":97,"locator":98},"MicroStrain 3DM-GX3","Microstrain 3DM-GX3","used in the second-generation handheld Zebedee, mounted on the back of the laser","Sec. IV-D",{"category":90,"model":100,"canonical":100,"role":101,"dataset":59,"specs":102,"locator":103},"second IMU on the sensor base (model not stated)","reference or ground truth","measures base motion as input for spring system identification","Sec. II-A",{"category":84,"model":105,"canonical":105,"role":101,"dataset":59,"specs":106,"locator":107},"SICK LMS291 (spinning)","rotated at 1 Hz about its middle scan ray, mounted at 750 mm on a pushcart, 13 500 points per second, hemispherical FoV facing behind the cart","Sec. IV; Fig. 2(b)",{"category":109,"model":110,"canonical":110,"role":101,"dataset":59,"specs":111,"locator":112},"other","Vicon motion capture system (14 cameras)","millimeter position precision within about 2 x 2 m; about 1 deg tag orientation precision; 10 x 8 m room","Sec. IV-C",{"category":114,"model":115,"canonical":115,"role":86,"dataset":59,"specs":116,"locator":117},"mobile_scanner_device","Zebedee handheld, first generation","laser and IMU in a 150 g 3D-printed housing on a single spring (50 to 150 mm, 5 to 20 g); total mass well under 0.5 kg; tethered to a pushcart for power and logging in early tests","Sec. II; Sec. IV; Fig. 2(a), 2(b)",{"category":114,"model":119,"canonical":119,"role":86,"dataset":59,"specs":120,"locator":121},"Zebedee handheld, second generation","MicroStrain 3DM-GX3 IMU mounted on the back of the laser and a spring with slightly different physical characteristics; design developed with assistance from CMD Product Design and Innovation; used for the stairwell oscillation-stoppage experiment","Sec. IV-D; Fig. 2(d); Acknowledgment",{"category":123,"model":124,"canonical":124,"role":86,"dataset":59,"specs":125,"locator":126},"platform","John Deere Gator TE electric vehicle","automated electric vehicle carrying the dual-spring vehicle-mounted Zebedee; driven off-road to provide inputs for spring system identification; no vehicle SLAM results are reported","Sec. II-A; Fig. 1(b)",{"category":123,"model":128,"canonical":128,"role":86,"dataset":59,"specs":129,"locator":107},"wheeled pushcart","carries the spinning SICK reference and power and logging for the first-generation handheld",{"category":131,"model":132,"canonical":132,"role":133,"dataset":59,"specs":134,"locator":135},"compute","3.2 GHz Intel Xeon CPU","compute for runtime","MATLAB with C++ MEX; open-loop at about 62 to 73 % of acquisition time","Sec. IV-B",[],{"totalRows":138,"groupCount":139,"groups":140,"others":464},120,20,[141,282,338,421],{"slug":142,"group":143,"sourceId":144,"sourceLabel":145,"table":146,"selfRows":147,"metrics":148,"seqs":160,"entrants":175,"cells":181,"outcomes":275,"locators":276,"hardware":278,"wordings":279,"notes":280},"elasticity-ct2022-table-v","elasticity_ct2022:Table V","elasticity_ct2022","Park et al., 2022","Table V",24,[149,154,156,159],{"label":150,"unit":151,"statistic":152,"alignment":153},"Position Err. (projective distance)","mm","mean","not_applicable",{"label":150,"unit":151,"statistic":155,"alignment":153},"std",{"label":157,"unit":158,"statistic":152,"alignment":153},"Normal Err.","rad",{"label":157,"unit":158,"statistic":155,"alignment":153},[161,165,167,169,171,173],{"dataset":162,"sequence":163,"environment":164},"authors' real datasets","patch a","planar floor or wall patch",{"dataset":162,"sequence":166,"environment":164},"patch b",{"dataset":162,"sequence":168,"environment":164},"patch c",{"dataset":162,"sequence":170,"environment":164},"patch d",{"dataset":162,"sequence":172,"environment":164},"patch f",{"dataset":162,"sequence":174,"environment":164},"patch g",[176,179],{"name":177,"methodId":5,"linkable":178,"proposed":78,"self":178},"CT-SLAM [3] (raw points)",true,{"name":180,"methodId":144,"linkable":178,"proposed":178,"self":78},"Proposed (fused surfels)",[182,186,189,192,195,197,199,201,203,205,207,209,211,213,215,217,219,221,223,225,226,227,229,230,231,233,235,236,238,240,242,244,246,249,251,253,255,256,257,259,260,263,265,267,269,271,273,274],[183,183,183,184,185,183,185,185,183],0,8.2,-1,[183,187,183,188,185,183,185,185,183],1,14.8,[183,190,183,191,185,183,185,185,183],2,0.101,[183,193,183,194,185,183,185,185,183],3,0.082,[187,183,183,196,185,183,185,185,183],3.4,[187,187,183,198,185,183,185,185,183],4.7,[187,190,183,200,185,183,185,185,183],0.069,[187,193,183,202,185,183,185,185,183],0.075,[183,183,187,204,185,183,185,185,183],9.3,[183,187,187,206,185,183,185,185,183],16.8,[183,190,187,208,185,183,185,185,183],0.12,[183,193,187,210,185,183,185,185,183],0.116,[187,183,187,212,185,183,185,185,183],3.2,[187,187,187,214,185,183,185,185,183],4.4,[187,190,187,216,185,183,185,185,183],0.085,[187,193,187,218,185,183,185,185,183],0.076,[183,183,190,220,185,183,185,185,183],8.9,[183,187,190,222,185,183,185,185,183],17.3,[183,190,190,224,185,183,185,185,183],0.092,[183,193,190,208,185,183,185,185,183],[187,183,190,196,185,183,185,185,183],[187,187,190,228,185,183,185,185,183],6.2,[187,190,190,218,185,183,185,185,183],[187,193,190,194,185,183,185,185,183],[183,183,193,232,185,183,185,185,183],9.4,[183,187,193,234,185,183,185,185,183],17,[183,190,193,216,185,183,185,185,183],[183,193,193,237,185,183,185,185,183],0.099,[187,183,193,239,185,183,185,185,183],3.9,[187,187,193,241,185,183,185,185,183],5.3,[187,190,193,243,185,183,185,185,183],0.078,[187,193,193,245,185,183,185,185,183],0.08,[183,183,247,248,185,183,185,185,183],4,8,[183,187,247,250,185,183,185,185,183],13.7,[183,190,247,252,185,183,185,185,183],0.096,[183,193,247,254,185,183,185,185,183],0.087,[187,183,247,196,185,183,185,185,183],[187,187,247,198,185,183,185,185,183],[187,190,247,258,185,183,185,185,183],0.083,[187,193,247,245,185,183,185,185,183],[183,183,261,262,185,183,185,185,183],5,9.1,[183,187,261,264,185,183,185,185,183],16,[183,190,261,266,185,183,185,185,183],0.106,[183,193,261,268,185,183,185,185,183],0.115,[187,183,261,270,185,183,185,185,183],4.5,[187,187,261,272,185,183,185,185,183],6.5,[187,190,261,243,185,183,185,185,183],[187,193,261,194,185,183,185,185,183],[],[277],"Table V (VoR, p. 991; identical to arXiv v1 Table IV)",[],[],[281],"known planar patches; position error = projective distance to patch mean plane (mm), normal error in rad; CT-SLAM [3] cloud is undistorted by its globally optimised trajectory but unfused; no ground truth. Values read from the VoR Table V by the second checker",{"slug":283,"group":284,"sourceId":285,"sourceLabel":286,"table":287,"selfRows":288,"metrics":289,"seqs":296,"entrants":309,"cells":314,"outcomes":332,"locators":333,"hardware":334,"wordings":335,"notes":336},"sammartano2018zeb-table-5","sammartano2018zeb:Table 5","sammartano2018zeb","Sammartano & Spanò, 2018","Table 5",10,[290,294],{"label":291,"unit":292,"statistic":152,"alignment":293},"Mean","m","SE3",{"label":295,"unit":292,"statistic":155,"alignment":293},"St.dev",[297,301,303,305,307],{"dataset":298,"sequence":299,"environment":300},"Valperga castle","Tower (A), full raw roundtrip","narrow cylindrical tower",{"dataset":298,"sequence":302,"environment":300},"Tower (A), raw outward",{"dataset":298,"sequence":304,"environment":300},"Tower (A), raw return",{"dataset":298,"sequence":306,"environment":300},"Tower (A), optimised outward",{"dataset":298,"sequence":308,"environment":300},"Tower (A), optimised return",[310,312],{"name":311,"methodId":5,"linkable":178,"proposed":78,"self":178},"ZEB1",{"name":313,"methodId":5,"linkable":178,"proposed":78,"self":178},"ZEB1 (optimised)",[315,317,319,321,322,324,326,327,329,331],[183,183,183,316,185,183,185,185,183],0.025,[183,187,183,318,185,183,185,185,183],0.034,[183,183,187,320,185,183,185,185,183],0.026,[183,187,187,318,185,183,185,185,183],[183,183,190,323,185,183,185,185,183],0.024,[183,187,190,325,185,183,185,185,183],0.032,[187,183,193,316,185,183,185,185,183],[187,187,193,328,185,183,185,185,183],0.03,[187,183,247,330,185,183,185,185,183],0.022,[187,187,247,316,185,183,185,185,183],[],[287],[],[],[337],"Tower (A), ZEB1 surfaces vs CRP reference model (about 1 cm accuracy), cloud-to-cloud best-fitting alignment.",{"slug":339,"group":340,"sourceId":5,"sourceLabel":6,"table":341,"selfRows":342,"metrics":343,"seqs":361,"entrants":380,"cells":393,"outcomes":407,"locators":413,"hardware":416,"wordings":418,"notes":419},"zebedee2012-text-sec-iv-b","zebedee2012:Text Sec.IV-B","Text Sec.IV-B",9,[344,348,352,355,358],{"label":345,"unit":346,"statistic":155,"alignment":347},"standard deviation of point-cloud errors vs reference cloud","cm","not_reported",{"label":349,"unit":350,"statistic":347,"alignment":351},"open-loop positional drift envelope relative to the closed-loop solution","cm\u002Fmin","first-pose",{"label":353,"unit":354,"statistic":347,"alignment":351},"open-loop yaw drift envelope relative to the closed-loop solution","deg\u002Fmin",{"label":356,"unit":357,"statistic":347,"alignment":347},"open-loop processing time as share of acquisition time","%",{"label":359,"unit":360,"statistic":347,"alignment":347},"global optimization run time","min",[362,366,369,372,375,378],{"dataset":363,"sequence":364,"environment":365},"authors' mobile mapping experiments","office","indoor open-plan office with cluttered desks",{"dataset":363,"sequence":367,"environment":368},"courtyard","outdoor courtyard with vegetation, walls and a staircase",{"dataset":363,"sequence":370,"environment":371},"courtyard (repeated runs)","outdoor courtyard",{"dataset":363,"sequence":373,"environment":374},"office and courtyard","indoor and outdoor",{"dataset":363,"sequence":376,"environment":377},"office (3.5 min acquisition)","indoor open-plan office",{"dataset":363,"sequence":379,"environment":371},"courtyard (6.5 min acquisition)",[381,383,385,387,389,391],{"name":382,"methodId":5,"linkable":178,"proposed":178,"self":178},"Zebedee SLAM closed-loop",{"name":384,"methodId":5,"linkable":178,"proposed":178,"self":178},"Zebedee SLAM open-loop",{"name":386,"methodId":5,"linkable":178,"proposed":178,"self":178},"Zebedee SLAM open-loop (0 fixed views)",{"name":388,"methodId":5,"linkable":178,"proposed":178,"self":178},"Zebedee SLAM open-loop (2 fixed views)",{"name":390,"methodId":5,"linkable":178,"proposed":178,"self":178},"Zebedee SLAM open-loop (5 fixed views)",{"name":392,"methodId":5,"linkable":178,"proposed":178,"self":178},"Zebedee SLAM global registration",[394,395,397,398,399,401,403,405,406],[183,183,183,239,185,183,185,185,183],[183,183,187,396,185,183,185,185,183],4.1,[187,187,190,59,183,187,185,185,183],[187,190,190,59,187,187,185,185,183],[190,193,193,400,190,190,183,185,183],62,[193,193,193,402,190,190,183,185,183],71,[247,193,193,404,190,190,183,185,183],73,[261,247,247,187,193,190,183,185,183],[261,247,261,190,247,190,183,185,183],[408,409,410,411,412],"range: about 15 to 25 cm\u002Fmin","range: about 0.4 to 1.5 deg\u002Fmin","approximate","upper bound: under 1 min","upper bound: under 2 min",[414,415,135],"Sec. IV-B; Fig. 6(b)","Sec. 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