[{"data":1,"prerenderedAt":807},["ShallowReactive",2],{"method-dlo2022":3},{"method":4,"reference":55,"equipment":77,"figures":118,"results":119},{"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":34,"estimator":37,"association":38,"timeModel":39,"deskew":40,"loopClosure":41,"globalOptimization":41,"mapRepresentation":42,"prior":41,"outputGeometry":43,"compute":44,"codeUrl":45,"codeLicense":46,"relatedVersions":47},"dlo2022","Chen et al., 2022a","DLO","Direct LiDAR Odometry: Fast Localization With Dense Point Clouds",2022,"recent","C05","odometry_with_local_mapping","DLO 採「速度優先」設計，直接使用輕度降採樣的稠密點雲，以自製 NanoGICP 先做相鄰掃描配準、再對由關鍵影格組成的子地圖配準。子地圖不以半徑搜尋點，而是在關鍵影格空間中選取最近鄰與凸包關鍵影格拼接，使遠處結構也能參與配準。IMU 僅選擇性提供旋轉初值（鬆耦合），且作者明言未做運動畸變校正。","Speed-first dense LiDAR odometry using two-stage GICP and keyframe-space submapping (nearest plus convex-hull keyframes), with only an optional IMU rotational prior and no deskewing.","full_text_reviewed","peer_reviewed_published","background","作者在肯塔基州列星頓的地下石灰岩礦坑、洛杉磯三層廢棄地鐵站與 DARPA SubT 城市賽道（華盛頓州 Elma 的廢棄電廠）測試，另以 Team Explorer 提供的 Mega Cavern 資料建圖（估計軌跡 9057.66 m），屬地下或既有設施情境；未在施工中工地測試。",[20,21],"public_benchmark","underground_or_tunnel",[23,24,25,26],"Keyframe-based submapping reduced positional error and processing time versus radius-based submapping on SubT Alpha course (Sec. III-A-1)","Primary state estimation for Team CoSTAR aerial vehicles in DARPA SubT (Sec. I)","Lowest APE (max, mean, std) and ME on both Alpha and Beta courses with less than one CPU core used, versus BLAM, Cartographer, LIO-Mapping, LOAM and LOCUS (Table III)","NanoGICP converged in 42.53 ms on average versus 72.88 ms for FastGICP and 178.24 ms for PCL GICP (Sec. III-A-3)",[28,29,30],"No motion distortion correction; listed with tighter IMU integration as future work (Sec. II-B; Sec. IV)","LIO-SAM and LVI-SAM could not be compared due to calibration and input requirements (Sec. III-B)","Competitor numbers and ground truth in Table III were retrieved from the LOCUS paper rather than rerun by the authors (Sec. III-B)",[32,33],"3D LiDAR (Ouster OS1, Velodyne VLP-16)","IMU (optional, gyroscope rotational prior only; VectorNav VN-100 on the field platforms and in the SubT Alpha dataset)",[35,36],"UAV","legged","two-stage GICP optimization (scan-to-scan then scan-to-map) with optional loosely-coupled IMU rotational prior","GICP on minimally preprocessed dense clouds (1 m box filter, 0.25 m voxel filter) using custom NanoGICP with data-structure reuse","discrete poses","none (authors state they do not correct motion distortion, Sec. II-B)","none","keyframe database; submap built by concatenating k-nearest and convex-hull keyframe clouds (adaptive keyframing via spaciousness metric)","pose estimates and keyframe-based map; export format not_reported","CPU; average 21.9 ms per scan and 9.5% CPU load with full data-structure recycling on a 4-core i7 1.30 GHz (Sec. III-A); field platforms ran on an Intel NUC Board NUC7i7DNBE 1.9 GHz (Sec. III-C); benchmark CPU usage 0.92 cores max and 0.62 cores mean (Table III)","https:\u002F\u002Fgithub.com\u002Fvectr-ucla\u002Fdirect_lidar_odometry","MIT (LICENSE file checked)",[48,52],{"relation":49,"title":50,"doi_or_url":51},"preprint","Direct LiDAR Odometry: Fast Localization with Dense Point Clouds (arXiv v3)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2110.00605",{"relation":53,"title":54,"doi_or_url":45},"code_release","vectr-ucla\u002Fdirect_lidar_odometry",{"id":5,"kind":56,"shortName":7,"title":8,"authors":57,"year":9,"venue":62,"venueType":63,"publisher":64,"volumeIssuePages":65,"doi":66,"arxivId":67,"url":68,"firstPublicDate":69,"publicationStatus":16,"metadataStatus":70,"fulltextStatus":15,"era":10,"classicReason":71,"codeUrl":45,"cluster":11,"topics":72,"mdpi":73,"verification":74,"label":6,"fulltextRoute":75,"versionRead":76,"addedByCensus":73},"method",[58,59,60,61],"Kenny Chen","Brett T. Lopez","Ali-akbar Agha-mohammadi","Ankur Mehta","IEEE Robotics and Automation Letters","journal","IEEE","7(2):2000-2007","10.1109\u002Flra.2022.3142739","2110.00605","https:\u002F\u002Fapi.crossref.org\u002Fworks\u002F10.1109\u002FLRA.2022.3142739","2021-10-01","metadata_verified","not_applicable",[11],false,"confirmed","arXiv","arXiv v3 (2022-01-07), RA-L preprint version accepted January 2022; IEEE version of record not read",[78,85,89,95,100,104,109,113,117],{"category":79,"model":80,"canonical":80,"role":81,"dataset":82,"specs":83,"locator":84},"lidar","Ouster OS1","method input",null,"360 deg LiDAR, 20 Hz as stated; on custom quadrotor","Fig. 1A; Sec. II-B; Sec. III-C",{"category":79,"model":86,"canonical":86,"role":81,"dataset":82,"specs":87,"locator":88},"Velodyne VLP-16","360 deg LiDAR, 10 Hz; with protective guards on Spot","Fig. 1B; Sec. II-B; Sec. III-C",{"category":90,"model":91,"canonical":92,"role":81,"dataset":82,"specs":93,"locator":94},"imu","VectorNav VN-100","VectorNav VN100","rigidly mounted below the LiDAR base on both field platforms; gyroscope used for optional rotational prior","Sec. III-C",{"category":96,"model":97,"canonical":97,"role":81,"dataset":82,"specs":98,"locator":99},"platform","custom quadrotor (Team CoSTAR)","carries Ouster OS1","Fig. 1A",{"category":96,"model":101,"canonical":101,"role":81,"dataset":82,"specs":102,"locator":103},"Boston Dynamics Spot","legged robot with custom payload","Fig. 1B",{"category":105,"model":106,"canonical":106,"role":107,"dataset":82,"specs":108,"locator":94},"compute","Intel NUC Board NUC7i7DNBE","compute for runtime","1.9 GHz CPU",{"category":105,"model":110,"canonical":110,"role":107,"dataset":82,"specs":111,"locator":112},"4-core Intel i7 1.30 GHz CPU","4-core, 1.30 GHz","Sec. III-A",{"category":79,"model":86,"canonical":86,"role":114,"dataset":115,"specs":116,"locator":112},"dataset sensor","DARPA SubT Urban Circuit Alpha Course","not_reported",{"category":90,"model":91,"canonical":92,"role":114,"dataset":115,"specs":116,"locator":112},[],{"totalRows":120,"groupCount":121,"groups":122,"others":741},73,15,[123,268,440,616],{"slug":124,"group":125,"sourceId":126,"sourceLabel":127,"table":128,"selfRows":129,"metrics":130,"seqs":140,"entrants":151,"cells":162,"outcomes":262,"locators":263,"hardware":264,"wordings":265,"notes":266},"yin2023semanticbimloc-table-6","yin2023semanticbimloc:Table 6","yin2023semanticbimloc","Yin et al., 2023","Table 6",12,[131,135,138],{"label":132,"unit":133,"statistic":134,"alignment":116},"Tr. (m), 2D translation RMSE","m","RMSE",{"label":136,"unit":137,"statistic":134,"alignment":116},"Rt. (deg), yaw RMSE","deg",{"label":139,"unit":133,"statistic":116,"alignment":41},"Delta Z (m) = Z_last - Z_init",[141,145,147,149],{"dataset":142,"sequence":143,"environment":144},"self-collected NUS SDE4 sequences","2-3","completed six-storey university building (NUS SDE4), corridors and lounges, storeys 2-5",{"dataset":142,"sequence":146,"environment":144},"3-3",{"dataset":142,"sequence":148,"environment":144},"4-2",{"dataset":142,"sequence":150,"environment":144},"5-2",[152,156,158,160],{"name":153,"methodId":154,"linkable":155,"proposed":73,"self":73},"LOAM [5] (A-LOAM code)","aloam_software",true,{"name":157,"methodId":5,"linkable":155,"proposed":73,"self":155},"DLO [55]",{"name":159,"methodId":82,"linkable":73,"proposed":73,"self":73},"Open3D SLAM [56]",{"name":161,"methodId":126,"linkable":155,"proposed":155,"self":73},"BIM-based Localization (proposed)",[163,167,170,173,175,177,179,181,183,185,188,190,192,194,196,198,200,202,204,206,208,210,211,213,215,217,219,221,223,225,227,229,231,233,235,237,239,241,243,245,247,249,251,253,255,257,258,260],[164,164,164,165,166,164,166,166,164],0,0.058,-1,[164,168,164,169,166,164,166,166,164],1,0.296,[164,171,164,172,166,164,166,166,164],2,-0.968,[164,164,168,174,166,164,166,166,164],0.04,[164,168,168,176,166,164,166,166,164],0.302,[164,171,168,178,166,164,166,166,164],-0.01,[164,164,171,180,166,164,166,166,164],0.038,[164,168,171,182,166,164,166,166,164],0.326,[164,171,171,184,166,164,166,166,164],-0.793,[164,164,186,187,166,164,166,166,164],3,0.017,[164,168,186,189,166,164,166,166,164],0.303,[164,171,186,191,166,164,166,166,164],-0.938,[168,164,164,193,166,164,166,166,164],0.062,[168,168,164,195,166,164,166,166,164],0.263,[168,171,164,197,166,164,166,166,164],-2.675,[168,164,168,199,166,164,166,166,164],0.034,[168,168,168,201,166,164,166,166,164],0.396,[168,171,168,203,166,164,166,166,164],-0.025,[168,164,171,205,166,164,166,166,164],0.036,[168,168,171,207,166,164,166,166,164],0.291,[168,171,171,209,166,164,166,166,164],-0.981,[168,164,186,187,166,164,166,166,164],[168,168,186,212,166,164,166,166,164],0.182,[168,171,186,214,166,164,166,166,164],-1.149,[171,164,164,216,166,164,166,166,164],0.1,[171,168,164,218,166,164,166,166,164],0.28,[171,171,164,220,166,164,166,166,164],-1.547,[171,164,168,222,166,164,166,166,164],0.046,[171,168,168,224,166,164,166,166,164],0.662,[171,171,168,226,166,164,166,166,164],0.015,[171,164,171,228,166,164,166,166,164],0.041,[171,168,171,230,166,164,166,166,164],0.276,[171,171,171,232,166,164,166,166,164],-0.874,[171,164,186,234,166,164,166,166,164],0.033,[171,168,186,236,166,164,166,166,164],0.215,[171,171,186,238,166,164,166,166,164],-0.709,[186,164,164,240,166,164,166,166,164],0.077,[186,168,164,242,166,164,166,166,164],0.59,[186,171,164,244,166,164,166,166,164],0.084,[186,164,168,246,166,164,166,166,164],0.03,[186,168,168,248,166,164,166,166,164],0.359,[186,171,168,250,166,164,166,166,164],-0.002,[186,164,171,252,166,164,166,166,164],0.079,[186,168,171,254,166,164,166,166,164],1.198,[186,171,171,256,166,164,166,166,164],-0.052,[186,164,186,205,166,164,166,166,164],[186,168,186,259,166,164,166,166,164],0.345,[186,171,186,261,166,164,166,166,164],0.083,[],[128],[],[],[267],"LiDAR-only comparison (no IMU for any method) on four sequences; 2D RMSE against the Cartographer 2D reference; Delta Z = mean height of the last 50 poses minus that of the first 50 poses, planar motion assumed",{"slug":269,"group":270,"sourceId":5,"sourceLabel":6,"table":271,"selfRows":272,"metrics":273,"seqs":290,"entrants":301,"cells":316,"outcomes":434,"locators":435,"hardware":436,"wordings":437,"notes":438},"dlo2022-table-iii","dlo2022:Table III","Table III",10,[274,277,280,283,285,288],{"label":275,"unit":133,"statistic":276,"alignment":116},"APE [m] max","max",{"label":278,"unit":133,"statistic":279,"alignment":116},"APE [m] mean","mean",{"label":281,"unit":133,"statistic":282,"alignment":116},"APE [m] std","std",{"label":284,"unit":133,"statistic":134,"alignment":116},"ME [m] rmse (text: mean error)",{"label":286,"unit":287,"statistic":276,"alignment":71},"CPU Usage, No. of Cores, max","cores",{"label":289,"unit":287,"statistic":279,"alignment":71},"CPU Usage, No. of Cores, mean",[291,295,298],{"dataset":292,"sequence":293,"environment":294},"DARPA SubT Urban Circuit (Alpha and Beta courses)","Alpha Course (757.4 m)","abandoned power plant, Elma WA (subterranean-like urban)",{"dataset":292,"sequence":296,"environment":297},"Beta Course (631.5 m)","DARPA SubT Urban Circuit, Beta course (site not described in the paper)",{"dataset":292,"sequence":299,"environment":300},"Alpha and Beta (not separated)","DARPA SubT Urban Circuit, Alpha and Beta courses",[302,304,307,310,313,315],{"name":303,"methodId":82,"linkable":73,"proposed":73,"self":73},"BLAM [12]",{"name":305,"methodId":306,"linkable":155,"proposed":73,"self":73},"Cartographer [19]","cartographer2016",{"name":308,"methodId":309,"linkable":155,"proposed":73,"self":73},"LIO-Mapping [5]","liomapping2019",{"name":311,"methodId":312,"linkable":155,"proposed":73,"self":73},"LOAM [10]","loam2014",{"name":314,"methodId":82,"linkable":73,"proposed":73,"self":73},"LOCUS [13]",{"name":7,"methodId":5,"linkable":155,"proposed":155,"self":155},[317,319,321,323,325,327,329,331,333,336,339,341,343,345,347,349,351,353,355,357,359,361,363,365,367,368,370,372,374,376,378,380,382,384,386,388,390,392,393,395,397,399,401,403,404,406,408,410,412,414,416,418,419,421,423,425,427,429,430,432],[164,164,164,318,166,164,166,166,164],3.44,[164,168,164,320,166,164,166,166,164],1.01,[164,171,164,322,166,164,166,166,164],0.94,[164,186,164,324,166,164,166,166,164],0.43,[164,164,168,326,166,164,166,166,164],3.89,[164,168,168,328,166,164,166,166,164],2.27,[164,171,168,330,166,164,166,166,164],0.89,[164,186,168,332,166,164,166,166,164],1.27,[164,334,171,335,166,164,166,166,164],4,1.14,[164,337,171,338,166,164,166,166,164],5,0.93,[168,164,164,340,166,164,166,166,164],5.84,[168,168,164,342,166,164,166,166,164],2.91,[168,171,164,344,166,164,166,166,164],1.6,[168,186,164,346,166,164,166,166,164],1.05,[168,164,168,348,166,164,166,166,164],2.64,[168,168,168,350,166,164,166,166,164],1.37,[168,171,168,352,166,164,166,166,164],0.67,[168,186,168,354,166,164,166,166,164],0.31,[168,334,171,356,166,164,166,166,164],1.75,[168,337,171,358,166,164,166,166,164],0.88,[171,164,164,360,166,164,166,166,164],2.12,[171,168,164,362,166,164,166,166,164],0.99,[171,171,164,364,166,164,166,166,164],0.51,[171,186,164,366,166,164,166,166,164],0.45,[171,164,168,344,166,164,166,166,164],[171,168,168,369,166,164,166,166,164],1.18,[171,171,168,371,166,164,166,166,164],0.22,[171,186,168,373,166,164,166,166,164],0.61,[171,334,171,375,166,164,166,166,164],1.8,[171,337,171,377,166,164,166,166,164],1.53,[186,164,164,379,166,164,166,166,164],4.33,[186,168,164,381,166,164,166,166,164],1.38,[186,171,164,383,166,164,166,166,164],1.19,[186,186,164,385,166,164,166,166,164],0.6,[186,164,168,387,166,164,166,166,164],2.58,[186,168,168,389,166,164,166,166,164],2.11,[186,171,168,391,166,164,166,166,164],0.44,[186,186,168,362,166,164,166,166,164],[186,334,171,394,166,164,166,166,164],1.65,[186,337,171,396,166,164,166,166,164],1.41,[334,164,164,398,166,164,166,166,164],0.63,[334,168,164,400,166,164,166,166,164],0.26,[334,171,164,402,166,164,166,166,164],0.18,[334,186,164,218,166,164,166,166,164],[334,164,168,405,166,164,166,166,164],1.2,[334,168,168,407,166,164,166,166,164],0.58,[334,171,168,409,166,164,166,166,164],0.39,[334,186,168,411,166,164,166,166,164],0.48,[334,334,171,413,166,164,166,166,164],3.39,[334,337,171,415,166,164,166,166,164],2.72,[337,164,164,417,166,164,166,166,164],0.4,[337,168,164,402,166,164,166,166,164],[337,171,164,420,166,164,166,166,164],0.06,[337,186,164,422,166,164,166,166,164],0.19,[337,164,168,424,166,164,166,166,164],0.5,[337,168,168,426,166,164,166,166,164],0.16,[337,171,168,428,166,164,166,166,164],0.09,[337,186,168,422,166,164,166,166,164],[337,334,171,431,166,164,166,166,164],0.92,[337,337,171,433,166,164,166,166,164],0.62,[],[271],[],[],[439],"DARPA SubT Urban Circuit Alpha and Beta courses; competitor numbers and ground truth retrieved from the LOCUS paper [13], not rerun; APE max\u002Fmean\u002Fstd and ME rmse in m; CPU usage in number of cores",{"slug":441,"group":442,"sourceId":443,"sourceLabel":444,"table":445,"selfRows":272,"metrics":446,"seqs":449,"entrants":472,"cells":488,"outcomes":610,"locators":611,"hardware":612,"wordings":613,"notes":614},"glim2024-table-v","glim2024:Table V","glim2024","Koide et al., 2024","Table V",[447],{"label":448,"unit":133,"statistic":116,"alignment":116},"Absolute Trajectory Error [m] (no loop closure)",[450,454,456,458,460,462,464,466,468,470],{"dataset":451,"sequence":452,"environment":453},"Multi-Camera Newer College","quad-easy","handheld campus indoor and outdoor",{"dataset":451,"sequence":455,"environment":453},"quad-medium",{"dataset":451,"sequence":457,"environment":453},"quad-hard",{"dataset":451,"sequence":459,"environment":453},"stairs",{"dataset":451,"sequence":461,"environment":453},"park",{"dataset":451,"sequence":463,"environment":453},"cloister",{"dataset":451,"sequence":465,"environment":453},"math-easy",{"dataset":451,"sequence":467,"environment":453},"math-medium",{"dataset":451,"sequence":469,"environment":453},"math-hard",{"dataset":451,"sequence":471,"environment":453},"Average",[473,476,479,482,484,486],{"name":474,"methodId":475,"linkable":155,"proposed":73,"self":73},"LINS [3]","lins2020",{"name":477,"methodId":478,"linkable":155,"proposed":73,"self":73},"LIO-SAM [10] (without loop closure; unlabeled row above LIO-SAM)","liosam2020",{"name":480,"methodId":481,"linkable":155,"proposed":73,"self":73},"FAST-LIO2 [5]","fastlio2_2022",{"name":483,"methodId":82,"linkable":73,"proposed":73,"self":73},"CLINS [11] (without loop closure; unlabeled row above CLINS)",{"name":485,"methodId":5,"linkable":155,"proposed":73,"self":155},"DLO [14]",{"name":487,"methodId":443,"linkable":155,"proposed":155,"self":73},"GLIM (odometry, without loop closure; unlabeled row above GLIM)",[489,490,492,494,496,498,500,503,506,509,512,514,516,518,520,522,524,526,528,530,532,534,536,538,540,542,544,546,548,549,551,553,555,557,559,561,563,565,567,569,571,573,574,576,578,580,582,584,586,588,590,592,594,596,598,600,602,604,606,608],[164,164,164,426,166,164,166,166,164],[164,164,168,491,166,164,166,166,164],0.212,[164,164,171,493,166,164,166,166,164],16.824,[164,164,186,495,166,164,166,166,164],3.405,[164,164,334,497,166,164,166,166,164],0.612,[164,164,337,499,166,164,166,166,164],1.17,[164,164,501,502,166,164,166,166,164],6,0.216,[164,164,504,505,166,164,166,166,164],7,0.259,[164,164,507,508,166,164,166,166,164],8,5.71,[164,164,510,511,166,164,166,166,164],9,3.174,[168,164,164,513,166,164,166,166,164],0.086,[168,164,168,515,166,164,166,166,164],0.069,[168,164,171,517,166,164,166,166,164],0.105,[168,164,186,519,166,164,166,166,164],3.438,[168,164,334,521,166,164,166,166,164],1.381,[168,164,337,523,166,164,166,166,164],0.085,[168,164,501,525,166,164,166,166,164],0.088,[168,164,504,527,166,164,166,166,164],0.114,[168,164,507,529,166,164,166,166,164],0.089,[168,164,510,531,166,164,166,166,164],0.606,[171,164,164,533,166,164,166,166,164],0.068,[171,164,168,535,166,164,166,166,164],0.059,[171,164,171,537,166,164,166,166,164],0.05,[171,164,186,539,166,164,166,166,164],1.32,[171,164,334,541,166,164,166,166,164],0.319,[171,164,337,543,166,164,166,166,164],0.078,[171,164,501,545,166,164,166,166,164],0.091,[171,164,504,547,166,164,166,166,164],0.134,[171,164,507,165,166,164,166,166,164],[171,164,510,550,166,164,166,166,164],0.242,[186,164,164,552,166,164,166,166,164],0.197,[186,164,168,554,166,164,166,166,164],0.674,[186,164,171,556,166,164,166,166,164],0.151,[186,164,186,558,166,164,166,166,164],2.336,[186,164,334,560,166,164,166,166,164],18.265,[186,164,337,562,166,164,166,166,164],0.293,[186,164,501,564,166,164,166,166,164],0.225,[186,164,504,566,166,164,166,166,164],0.235,[186,164,507,568,166,164,166,166,164],0.494,[186,164,510,570,166,164,166,166,164],2.541,[334,164,164,572,166,164,166,166,164],0.08,[334,164,168,216,166,164,166,166,164],[334,164,171,575,166,164,166,166,164],0.168,[334,164,186,577,166,164,166,166,164],0.129,[334,164,334,579,166,164,166,166,164],0.686,[334,164,337,581,166,164,166,166,164],0.163,[334,164,501,583,166,164,166,166,164],0.161,[334,164,504,585,166,164,166,166,164],0.211,[334,164,507,587,166,164,166,166,164],0.192,[334,164,510,589,166,164,166,166,164],0.21,[337,164,164,591,166,164,166,166,164],0.07,[337,164,168,593,166,164,166,166,164],0.061,[337,164,171,595,166,164,166,166,164],0.044,[337,164,186,597,166,164,166,166,164],0.106,[337,164,334,599,166,164,166,166,164],0.459,[337,164,337,601,166,164,166,166,164],0.063,[337,164,501,603,166,164,166,166,164],0.096,[337,164,504,605,166,164,166,166,164],0.119,[337,164,507,607,166,164,166,166,164],0.064,[337,164,510,609,166,164,166,166,164],0.12,[],[445],[],[],[615],"Multi-Camera Newer College (Ouster OS0-128, Alphasense Core); translational ATE; unlabeled rows are the no-loop-closure variant of the method printed in the next row (checked against the PDF layout and the ablation baseline in Table VIII)",{"slug":617,"group":618,"sourceId":619,"sourceLabel":620,"table":621,"selfRows":507,"metrics":622,"seqs":628,"entrants":642,"cells":654,"outcomes":734,"locators":735,"hardware":736,"wordings":738,"notes":739},"dlio2023-table-ii","dlio2023:Table II","dlio2023","Chen et al., 2023","Table II",[623,625],{"label":624,"unit":133,"statistic":116,"alignment":41},"End-to-End Translational Error",{"label":626,"unit":627,"statistic":279,"alignment":71},"Avg. 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