[{"data":1,"prerenderedAt":587},["ShallowReactive",2],{"method-iriom4d2023":3},{"method":4,"reference":55,"equipment":78,"figures":112,"results":152},{"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":34,"platform":37,"estimator":39,"association":40,"timeModel":41,"deskew":42,"loopClosure":43,"globalOptimization":44,"mapRepresentation":45,"prior":46,"outputGeometry":47,"compute":48,"codeUrl":49,"codeLicense":49,"relatedVersions":50},"iriom4d2023","Zhuang et al., 2023","4D iRIOM","4D iRIOM: 4D Imaging Radar Inertial Odometry and Mapping",2023,"recent","C07","full_slam_with_global_correction","4D iRIOM 以 4D 成像雷達加 IMU 做里程計與建圖：每張雷達掃描先用漸進非凸（GNC）方法估計自身速度，排除移動物與多路徑造成的離群點，再把稀疏雷達點與局部子地圖的多個鄰近點以協方差加權配準；兩類量測都送入迭代擴展卡爾曼濾波器更新。最後以 scancontext 偵測回訪並建立位姿圖做迴圈閉合，得到全域一致的雷達點雲地圖。作者指出 LiDAR 與相機在雨、霧等劣化環境表現下降，毫米波雷達則能在霧中量測距離、方向與都卜勒速度。","Radar-inertial odometry and mapping with a 4D imaging radar: GNC-based ego-velocity from each scan plus distribution-to-multi-distribution scan-to-submap matches fused in an iterated EKF, with scancontext loop closure and pose graph optimization for a consistent radar map.","full_text_reviewed","peer_reviewed_published","supplementary","施工現場常有粉塵、雨霧與照明不足；作者以雨霧中 LiDAR 與相機表現下降作為研究動機，本文可作為雷達慣性 SLAM 在劣化環境的代表方法（推論，論文未測試粉塵或施工現場）。測試場景包含校園建物周邊與地下停車場，但雷達點雲稀疏，只能提供定位與粗略地圖，無法取代 LiDAR 做點雲精度量測；論文也沒有施工現場資料。",[20,21,22],"independent_reference","public_benchmark","underground_or_tunnel",[24,25,26,27,28],"On three in-house sequences, APE translation RMSE 0.305 to 0.336 m for iRIOM versus 3.388 to 10.068 m for EKFRIO (Table I)","Accuracy comparable to LiDAR-inertial FastLIO-SLAM on sequences 1-2, with lower APE rotation RMSE (Table I)","Loop closure reduces vertical closure error on the two-lap building sequence from 5.073 m to 0.001 m (Table I)","Ablations show that fusing both ego-velocity and scan-to-submap matches avoids failures seen with either alone or with a constant-velocity model (Table III)","Real-time on a consumer laptop (Table II)",[30,31,32,33],"Radar scans are sparse (typically under 500 points) and noisy, so maps are sparse (Sec. III)","Scan-to-submap matching is the costliest step, with maximum per-scan time up to 380 ms (Table II)","Reference trajectory for the underground parking sequence comes from FastLIO-SLAM rather than GNSS\u002FINS (Sec. IV-B)","Code is not released in the paper",[35,36],"4D imaging radar (Continental ARS548)","IMU (EPSON G345 inside the Bynav X1-5H GNSS\u002FINS)",[38],"[\"ground robot (drive type not stated in the text)\", \"ColoRadar dataset sequences\"]","iterated extended Kalman filter with IMU propagation; state includes radar-IMU extrinsics and gravity; updates from radar ego-velocity and from scan-to-submap point matches; loop closure by scancontext detection with GICP constraints in a pose graph (Sec. III)","ego-velocity estimated from each radar scan by graduated non-convexity (GNC) with outlier relaxation and chi-square pruning at significance 0.05; scan-to-submap matching of sparse radar points against N = 5 nearest submap points weighted by covariance (distribution-to-multi-distribution) in an incrementally updated map (Sec. III)","discrete radar scans (about 15 Hz) with IMU propagation between scans; inputs are time-aligned radar scans and 6D IMU data (Sec. III-A)","not described","yes, scancontext place recognition with a descriptor threshold tuned for 4D radar; GICP relative constraints (Sec. III)","pose graph optimization with relative pose and loop constraints (Sec. III)","radar point submap for scan-to-submap matching; global radar point map after pose graph optimization (Figs. 1, 5)","none; radar-IMU extrinsics are part of the state","robot trajectory and sparse radar point cloud map","real time on a consumer laptop for about 15 Hz radar scans; mean total 13.41 to 50.77 ms per scan, dominated by scan-to-submap matching (Table II)",null,[51],{"relation":52,"title":53,"doi_or_url":54},"preprint","4D iRIOM arXiv v2 (accepted, proofread version; CC BY 4.0)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2303.13962v2",{"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":49,"cluster":11,"topics":72,"mdpi":73,"verification":74,"label":6,"fulltextRoute":75,"versionRead":76,"addedByCensus":77},"method",[58,59,60,61],"Yuan Zhuang","Binliang Wang","Jianzhu Huai","Miao Li","IEEE Robotics and Automation Letters","journal","IEEE","8(6), pp. 3246-3253","10.1109\u002Flra.2023.3266669","2303.13962","https:\u002F\u002Fdoi.org\u002F10.1109\u002FLRA.2023.3266669","2023-03-24","metadata_verified","not_applicable",[11],false,"corrected","arXiv","arXiv v2 (2023-04-03), stated as the proofread version accepted to RA-L; IEEE version of record not opened",true,[79,86,90,95,101,106],{"category":80,"model":81,"canonical":81,"role":82,"dataset":83,"specs":84,"locator":85},"radar","ARS548","method input","4D iRIOM in-house radar dataset","4D imaging radar; 15 Hz; 76-77 GHz; elevation AOV +-20 deg, azimuth AOV +-60 deg; azimuth resolution 0.2 deg, elevation resolution 0.1 deg; range about 300 m; distance accuracy 0.3 m","Sec. IV-A",{"category":87,"model":88,"canonical":88,"role":82,"dataset":83,"specs":89,"locator":85},"imu","G345 (built into Bynav X1-5H)","gyroscope bias stability 0.00075 deg\u002Fs; angular random walk 0.003 deg\u002Fsqrt(s); accelerometer bias stability 70 ug; velocity random walk 0.0005 m\u002Fsqrt(s^3)",{"category":91,"model":92,"canonical":92,"role":93,"dataset":83,"specs":94,"locator":85},"gnss","X1-5H","reference or ground truth","GNSS\u002FINS reference in open-sky areas; about 3 cm with RTK, 25 cm after a 10 s RTK outage",{"category":96,"model":97,"canonical":98,"role":99,"dataset":83,"specs":100,"locator":85},"lidar","VLP-16","Velodyne VLP-16","compared device","LiDAR on the robot used by the FastLIO-SLAM baseline and for the sequence-3 reference",{"category":102,"model":103,"canonical":103,"role":82,"dataset":83,"specs":104,"locator":105},"platform","ground robot (drive type not stated)","carries radar, GNSS\u002FINS, LiDAR and cameras","Fig. 3",{"category":107,"model":108,"canonical":108,"role":109,"dataset":83,"specs":110,"locator":111},"compute","consumer laptop","compute for runtime","real-time processing of about 15 Hz radar scans","Sec. IV; Table II",[113,126,136,144],{"refId":5,"refLabel":6,"fig":114,"whatZh":115,"license":116,"licenseUrl":117,"sourceUrl":118,"src":119,"width":120,"height":121,"thumb":122,"thumbWidth":123,"thumbHeight":124,"modified":125},"Fig. 1","籃球場資料的 iRIOM 建圖結果：只用雷達自身速度與 IMU 時的地圖，對照再加入雷達點配準後的地圖","CC BY 4.0","http:\u002F\u002Fcreativecommons.org\u002Flicenses\u002Fby\u002F4.0\u002F","https:\u002F\u002Farxiv.org\u002Fhtml\u002F2303.13962v2\u002F8.png","\u002Ffigure-files\u002Firiom4d2023\u002Ffig-1.webp",1294,749,"\u002Ffigure-files\u002Firiom4d2023\u002Ffig-1.thumb.webp",480,278,"converted to WebP",{"refId":5,"refLabel":6,"fig":127,"whatZh":128,"license":116,"licenseUrl":117,"sourceUrl":129,"src":130,"width":131,"height":132,"thumb":133,"thumbWidth":123,"thumbHeight":134,"modified":135},"Fig. 2","iRIOM 系統概觀：雷達點雲前處理、GNC 自身速度估計、掃描對子地圖配準、迭代 EKF 更新，以及 scancontext 迴圈閉合與位姿圖","https:\u002F\u002Farxiv.org\u002Fhtml\u002F2303.13962v2\u002Fresult\u002F2\u002F2.png","\u002Ffigure-files\u002Firiom4d2023\u002Ffig-2.webp",1400,646,"\u002Ffigure-files\u002Firiom4d2023\u002Ffig-2.thumb.webp",221,"resized to at most 1400 px wide and converted to WebP",{"refId":5,"refLabel":6,"fig":105,"whatZh":137,"license":116,"licenseUrl":117,"sourceUrl":138,"src":139,"width":140,"height":141,"thumb":142,"thumbWidth":123,"thumbHeight":143,"modified":125},"測試用地面機器人與感測器配置（ZED 相機與 RGB-D 相機未使用）","https:\u002F\u002Farxiv.org\u002Fhtml\u002F2303.13962v2\u002F3.png","\u002Ffigure-files\u002Firiom4d2023\u002Ffig-3.webp",632,739,"\u002Ffigure-files\u002Firiom4d2023\u002Ffig-3.thumb.webp",561,{"refId":5,"refLabel":6,"fig":145,"whatZh":146,"license":116,"licenseUrl":117,"sourceUrl":147,"src":148,"width":131,"height":149,"thumb":150,"thumbWidth":123,"thumbHeight":151,"modified":135},"Fig. 5","序列 2 偵測到的迴圈閉合與閉合誤差，以及 iRIOM 的建圖結果","https:\u002F\u002Farxiv.org\u002Fhtml\u002F2303.13962v2\u002F5.png","\u002Ffigure-files\u002Firiom4d2023\u002Ffig-5.webp",634,"\u002Ffigure-files\u002Firiom4d2023\u002Ffig-5.thumb.webp",217,{"totalRows":153,"groupCount":154,"groups":155,"others":586},103,4,[156,347,460,539],{"slug":157,"group":158,"sourceId":5,"sourceLabel":6,"table":159,"selfRows":160,"metrics":161,"seqs":182,"entrants":192,"cells":201,"outcomes":339,"locators":341,"hardware":343,"wordings":344,"notes":345},"iriom4d2023-table-i","iriom4d2023:Table I","Table I",36,[162,167,169,173,176,179],{"label":163,"unit":164,"statistic":165,"alignment":166},"Closure Error Hor","m","mean","none",{"label":168,"unit":164,"statistic":165,"alignment":166},"Closure Error Ver",{"label":170,"unit":164,"statistic":171,"alignment":172},"APE RMSE Trans","RMSE","not_reported",{"label":174,"unit":175,"statistic":171,"alignment":172},"APE RMSE Rot","deg",{"label":177,"unit":178,"statistic":171,"alignment":172},"RPE RMSE Trans","%",{"label":180,"unit":181,"statistic":171,"alignment":172},"RPE RMSE Rot","deg\u002Fm",[183,186,189],{"dataset":83,"sequence":184,"environment":185},"Seq. 1 (basketball court, 6 min)","outdoor basketball court surrounded by trees",{"dataset":83,"sequence":187,"environment":188},"Seq. 2 (Xinghu Building, two laps)","outdoor around a university building with vehicles and pedestrians",{"dataset":83,"sequence":190,"environment":191},"Seq. 3 (underground parking lot)","underground parking lot with narrow passageways and smooth ground and walls",[193,195,197,199],{"name":194,"methodId":49,"linkable":73,"proposed":73,"self":73},"EKFRIO",{"name":196,"methodId":5,"linkable":77,"proposed":77,"self":77},"iRIO (without loop closure)",{"name":198,"methodId":5,"linkable":77,"proposed":77,"self":77},"iRIOM",{"name":200,"methodId":49,"linkable":73,"proposed":73,"self":73},"FastLIO (FastLIO-SLAM with loop closure)",[202,206,209,212,215,217,220,222,224,226,228,230,232,234,235,237,239,240,241,243,245,247,249,251,253,255,257,259,261,263,265,267,269,271,273,275,277,279,281,283,285,286,288,290,291,293,295,297,299,301,303,305,307,308,310,312,314,316,318,320,321,323,324,326,328,330,332,334,335,336,337,338],[203,203,203,204,205,203,205,205,203],0,0.53,-1,[203,207,203,208,205,203,205,205,203],1,15.759,[203,210,203,211,205,203,205,205,203],2,5.616,[203,213,203,214,205,203,205,205,203],3,10.261,[203,154,203,216,205,203,205,205,203],0.055,[203,218,203,219,205,203,205,205,203],5,0.183,[207,203,203,221,205,203,205,205,203],0.029,[207,207,203,223,205,203,205,205,203],0.01,[207,210,203,225,205,203,205,205,203],0.313,[207,213,203,227,205,203,205,205,203],2.923,[207,154,203,229,205,203,205,205,203],0.04,[207,218,203,231,205,203,205,205,203],0.171,[210,203,203,233,205,203,205,205,203],0.027,[210,207,203,223,205,203,205,205,203],[210,210,203,236,205,203,205,205,203],0.305,[210,213,203,238,205,203,205,205,203],2.751,[210,154,203,229,205,203,205,205,203],[210,218,203,231,205,203,205,205,203],[213,203,203,242,205,203,205,205,203],0.024,[213,207,203,244,205,203,205,205,203],0.007,[213,210,203,246,205,203,205,205,203],0.147,[213,213,203,248,205,203,205,205,203],5.331,[213,154,203,250,205,203,205,205,203],0.025,[213,218,203,252,205,203,205,205,203],0.343,[203,203,207,254,205,203,205,205,203],17.194,[203,207,207,256,205,203,205,205,203],24.631,[203,210,207,258,205,203,205,205,203],10.068,[203,213,207,260,205,203,205,205,203],9.859,[203,154,207,262,205,203,205,205,203],0.026,[203,218,207,264,205,203,205,205,203],0.12,[207,203,207,266,205,203,205,205,203],1.517,[207,207,207,268,205,203,205,205,203],5.073,[207,210,207,270,205,203,205,205,203],1.518,[207,213,207,272,205,203,205,205,203],2.866,[207,154,207,274,205,203,205,205,203],0.022,[207,218,207,276,205,203,205,205,203],0.114,[210,203,207,278,205,203,205,205,203],0.279,[210,207,207,280,205,203,205,205,203],0.001,[210,210,207,282,205,203,205,205,203],0.336,[210,213,207,284,205,203,205,205,203],2.626,[210,154,207,274,205,203,205,205,203],[210,218,207,287,205,203,205,205,203],0.112,[213,203,207,289,205,203,205,205,203],0.151,[213,207,207,250,205,203,205,205,203],[213,210,207,292,205,203,205,205,203],0.372,[213,213,207,294,205,203,205,205,203],6.363,[213,154,207,296,205,203,205,205,203],0.02,[213,218,207,298,205,203,205,205,203],0.322,[203,203,210,300,205,203,205,205,203],3.741,[203,207,210,302,205,203,205,205,203],9.268,[203,210,210,304,205,203,205,205,203],3.388,[203,213,210,306,205,203,205,205,203],8.655,[203,154,210,219,205,203,205,205,203],[203,218,210,309,205,203,205,205,203],0.458,[207,203,210,311,205,203,205,205,203],0.051,[207,207,210,313,205,203,205,205,203],0.032,[207,210,210,315,205,203,205,205,203],0.303,[207,213,210,317,205,203,205,205,203],8.368,[207,154,210,319,205,203,205,205,203],0.132,[207,218,210,252,205,203,205,205,203],[210,203,210,322,205,203,205,205,203],0.05,[210,207,210,313,205,203,205,205,203],[210,210,210,325,205,203,205,205,203],0.307,[210,213,210,327,205,203,205,205,203],8.359,[210,154,210,329,205,203,205,205,203],0.131,[210,218,210,331,205,203,205,205,203],0.34,[213,203,210,333,205,203,205,205,203],0.012,[213,207,210,244,205,203,205,205,203],[213,210,210,49,203,203,205,205,203],[213,213,210,49,203,203,205,205,203],[213,154,210,49,203,203,205,205,203],[213,218,210,49,203,203,205,205,203],[340],"not_applicable ('-': FastLIO-SLAM is the reference trajectory on sequence 3)",[342],"Table I; Sec. IV-B",[],[],[346],"In-house ground-robot sequences with ARS548 radar and EPSON G345 IMU; reference: Bynav X1-5H GNSS\u002FINS for sequences 1-2 and FastLIO-SLAM (LiDAR-IMU) for sequence 3; closure errors are means of 10 runs; FastLIO = FastLIO-SLAM with loop closure using the VLP-16",{"slug":348,"group":349,"sourceId":5,"sourceLabel":6,"table":350,"selfRows":160,"metrics":351,"seqs":371,"entrants":375,"cells":377,"outcomes":452,"locators":453,"hardware":455,"wordings":457,"notes":458},"iriom4d2023-table-ii","iriom4d2023:Table II","Table II",[352,356,358,359,361,362,363,365,366,367,369,370],{"label":353,"unit":354,"statistic":355,"alignment":71},"IMU predict time","ms","max",{"label":353,"unit":354,"statistic":357,"alignment":71},"other: minimum",{"label":353,"unit":354,"statistic":165,"alignment":71},{"label":360,"unit":354,"statistic":355,"alignment":71},"Ego vel. update time",{"label":360,"unit":354,"statistic":357,"alignment":71},{"label":360,"unit":354,"statistic":165,"alignment":71},{"label":364,"unit":354,"statistic":355,"alignment":71},"Scan-to-Submap update time",{"label":364,"unit":354,"statistic":357,"alignment":71},{"label":364,"unit":354,"statistic":165,"alignment":71},{"label":368,"unit":354,"statistic":355,"alignment":71},"Total time",{"label":368,"unit":354,"statistic":357,"alignment":71},{"label":368,"unit":354,"statistic":165,"alignment":71},[372,373,374],{"dataset":83,"sequence":184,"environment":185},{"dataset":83,"sequence":187,"environment":188},{"dataset":83,"sequence":190,"environment":191},[376],{"name":198,"methodId":5,"linkable":77,"proposed":77,"self":77},[378,380,382,384,386,387,389,392,395,398,401,404,407,409,411,413,415,417,419,421,423,425,427,429,431,433,434,436,438,439,441,443,444,446,448,450],[203,203,203,379,205,203,203,205,203],10.66,[203,207,203,381,205,203,203,205,203],0.09,[203,210,203,383,205,203,203,205,203],0.24,[203,213,203,385,205,203,203,205,203],13.42,[203,154,203,322,205,203,203,205,203],[203,218,203,388,205,203,203,205,203],0.47,[203,390,203,391,205,203,203,205,203],6,379.18,[203,393,203,394,205,203,203,205,203],7,1.47,[203,396,203,397,205,203,203,205,203],8,48.69,[203,399,203,400,205,203,203,205,203],9,380.08,[203,402,203,403,205,203,203,205,203],10,1.72,[203,405,203,406,205,203,203,205,203],11,49.41,[203,203,207,408,205,203,203,205,203],9.53,[203,207,207,410,205,203,203,205,203],0.08,[203,210,207,412,205,203,203,205,203],0.19,[203,213,207,414,205,203,203,205,203],6.24,[203,154,207,416,205,203,203,205,203],0.06,[203,218,207,418,205,203,203,205,203],0.51,[203,390,207,420,205,203,203,205,203],284.55,[203,393,207,422,205,203,203,205,203],2.28,[203,396,207,424,205,203,203,205,203],50.07,[203,399,207,426,205,203,203,205,203],285.1,[203,402,207,428,205,203,203,205,203],3.13,[203,405,207,430,205,203,203,205,203],50.77,[203,203,210,432,205,203,203,205,203],4.89,[203,207,210,410,205,203,203,205,203],[203,210,210,435,205,203,203,205,203],0.18,[203,213,210,437,205,203,203,205,203],2.84,[203,154,210,322,205,203,203,205,203],[203,218,210,440,205,203,203,205,203],0.36,[203,390,210,442,205,203,203,205,203],119.29,[203,393,210,331,205,203,203,205,203],[203,396,210,445,205,203,203,205,203],12.87,[203,399,210,447,205,203,203,205,203],120.55,[203,402,210,449,205,203,203,205,203],0.75,[203,405,210,451,205,203,203,205,203],13.41,[],[454],"Table II; Sec. IV",[456],"consumer laptop (model not reported)",[],[459],"Timing statistics of iRIOM (max \u002F min and mean in ms) per radar scan on a consumer laptop",{"slug":461,"group":462,"sourceId":5,"sourceLabel":6,"table":463,"selfRows":464,"metrics":465,"seqs":471,"entrants":479,"cells":483,"outcomes":532,"locators":533,"hardware":535,"wordings":536,"notes":537},"iriom4d2023-table-iv","iriom4d2023:Table IV","Table IV",16,[466,467,469,470],{"label":163,"unit":164,"statistic":172,"alignment":166},{"label":468,"unit":164,"statistic":172,"alignment":166},"Closure Error Vert",{"label":170,"unit":164,"statistic":171,"alignment":172},{"label":174,"unit":175,"statistic":171,"alignment":172},[472,476],{"dataset":473,"sequence":474,"environment":475},"ColoRadar","2_23_2021_edgar_classroom_run0","indoor classroom",{"dataset":473,"sequence":477,"environment":478},"2_28_2021_outdoors_run0","outdoor",[480,481,482],{"name":194,"methodId":49,"linkable":73,"proposed":73,"self":73},{"name":196,"methodId":5,"linkable":77,"proposed":77,"self":77},{"name":198,"methodId":5,"linkable":77,"proposed":77,"self":77},[484,486,488,490,492,494,496,498,500,502,504,506,508,510,512,514,516,518,520,522,524,526,528,530],[203,203,203,485,205,203,205,205,203],15.732,[203,207,203,487,205,203,205,205,203],6.746,[203,210,203,489,205,203,205,205,203],5.048,[203,213,203,491,205,203,205,205,203],10.758,[207,203,203,493,205,203,205,205,203],7.309,[207,207,203,495,205,203,205,205,203],0.652,[207,210,203,497,205,203,205,205,203],2.377,[207,213,203,499,205,203,205,205,203],7.896,[210,203,203,501,205,203,205,205,203],0.351,[210,207,203,503,205,203,205,205,203],0.043,[210,210,203,505,205,203,205,205,203],0.916,[210,213,203,507,205,203,205,205,203],7.198,[203,203,207,509,205,203,205,205,203],0.619,[203,207,207,511,205,203,205,205,203],8.045,[203,210,207,513,205,203,205,205,203],2.353,[203,213,207,515,205,203,205,205,203],12.795,[207,203,207,517,205,203,205,205,203],1.254,[207,207,207,519,205,203,205,205,203],0.909,[207,210,207,521,205,203,205,205,203],0.61,[207,213,207,523,205,203,205,205,203],9.05,[210,203,207,525,205,203,205,205,203],0.185,[210,207,207,527,205,203,205,205,203],0.195,[210,210,207,529,205,203,205,205,203],0.385,[210,213,207,531,205,203,205,205,203],8.222,[],[534],"Table IV; Sec. IV-D",[],[],[538],"ColoRadar dataset sequences; closure error and APE RMSE of the radar methods against the dataset reference trajectory",{"slug":540,"group":541,"sourceId":5,"sourceLabel":6,"table":542,"selfRows":543,"metrics":544,"seqs":548,"entrants":552,"cells":563,"outcomes":579,"locators":580,"hardware":582,"wordings":583,"notes":584},"iriom4d2023-table-iii-failure-counts","iriom4d2023:Table III (failure counts)","Table III (failure counts)",15,[545],{"label":546,"unit":547,"statistic":71,"alignment":71},"F (number of failures in 10 repetitions)","count",[549,550,551],{"dataset":83,"sequence":184,"environment":185},{"dataset":83,"sequence":187,"environment":188},{"dataset":83,"sequence":190,"environment":191},[553,555,557,559,561],{"name":554,"methodId":5,"linkable":77,"proposed":77,"self":77},"iRIO variant: Vel.",{"name":556,"methodId":5,"linkable":77,"proposed":77,"self":77},"iRIO variant: S2M",{"name":558,"methodId":5,"linkable":77,"proposed":77,"self":77},"iRIO variant: Both",{"name":560,"methodId":5,"linkable":77,"proposed":77,"self":77},"iRIO variant: CVM",{"name":562,"methodId":5,"linkable":77,"proposed":77,"self":77},"iRIO variant: S2S",[564,565,566,567,568,569,570,571,572,573,574,575,576,577,578],[203,203,203,207,205,203,205,205,203],[207,203,203,154,205,203,205,205,203],[210,203,203,203,205,203,205,205,203],[213,203,203,218,205,203,205,205,203],[154,203,203,210,205,203,205,205,203],[203,203,207,207,205,203,205,205,203],[207,203,207,207,205,203,205,205,203],[210,203,207,203,205,203,205,205,203],[213,203,207,213,205,203,205,205,203],[154,203,207,210,205,203,205,205,203],[203,203,210,203,205,203,205,205,203],[207,203,210,213,205,203,205,205,203],[210,203,210,203,205,203,205,205,203],[213,203,210,213,205,203,205,205,203],[154,203,210,207,205,203,205,205,203],[],[581],"Table III; Sec. IV-C",[],[],[585],"Ablation on in-house sequences, number of failures F in 10 repetitions; Vel.: ego-velocity update only; S2M: scan-to-submap update only; Both: both updates (iRIO); CVM: constant velocity model replaces the IMU; S2S: scan-to-five-scans matching; error columns of this table omitted (row cap)",[],1790510660991]