[{"data":1,"prerenderedAt":1117},["ShallowReactive",2],{"method-kissicp2023":3},{"method":4,"reference":59,"equipment":84,"figures":92,"results":93},{"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":21,"limitations":26,"sensors":33,"platform":35,"estimator":40,"association":41,"timeModel":42,"deskew":43,"loopClosure":44,"globalOptimization":45,"mapRepresentation":46,"prior":44,"outputGeometry":47,"compute":48,"codeUrl":49,"codeLicense":50,"relatedVersions":51},"kissicp2023","Vizzo et al., 2023","KISS-ICP","KISS-ICP: In Defense of Point-to-Point ICP - Simple, Accurate, and Robust Registration If Done the Right Way",2023,"recent","C05","odometry_with_local_mapping","KISS-ICP 回歸最基本的點到點（point-to-point）ICP，僅保留等速運動預測與逐點去畸變、體素雙重降採樣、依運動模型偏差自適應的對應距離門檻，以及穩健核函數等少數元件。地圖為雜湊表中的降採樣體素點雲，並保留原始點座標以避免離散化誤差。作者主張在同一組參數下可用於車載、無人機、Segway 與手持 LiDAR，且不需 IMU，也不含迴圈或位姿圖。","A minimal LiDAR-only odometry built on robust point-to-point ICP with constant-velocity deskewing, double voxel downsampling, and an adaptive correspondence threshold, working across platforms with one parameter set.","full_text_reviewed","peer_reviewed_published","main_body","not_reported",[20],"public_benchmark",[22,23,24,25],"Only seven parameters and the same configuration across datasets (Table I; Sec. IV)","Constant-velocity deskewing performs on par with IMU-based velocity for deskewing on KITTI-raw in their test (Sec. IV-D; Table V)","Lowest relative and absolute errors on all MulRan sequences versus MULLS, SuMa and F-LOAM, e.g. KAIST ATE 17.40 m versus 37.24 m for MULLS (Table III)","Adaptive threshold matches or beats every fixed threshold on KITTI (average 0.50% versus 0.51 to 0.53%) (Table VI)",[27,28,29,30,31,32],"Assumes motion within a sweep is small (Sec. III-A)","Performance gap on Newer College long sequence attributed to CT-ICP loop closure (Sec. IV-C)","NCLT evaluation unreliable per the authors (ground-truth misalignment, missing frames); CT-ICP NCLT result could not be reproduced (Sec. IV-C)","No loop closure or pose-graph optimization; authors describe pose-graph optimization as orthogonal (Sec. II)","Reported to fail on all ENWIDE degenerate sequences (COIN-LIO, Sec. IV-C)","Reported to struggle with quadruped and backpack motion under constant-velocity model (RKO-LIO, Sec. II)",[34],"3D LiDAR only",[36,37,38,39],"vehicle","UAV","wheeled UGV","handheld","robust point-to-point ICP (Gauss-Newton with robust kernel) frame-to-local-map, constant-velocity motion prediction","point-to-point nearest neighbour with adaptive correspondence threshold derived from observed deviation from the motion model","discrete poses with per-point constant-velocity deskew","constant-velocity model applied with per-point relative timestamps (Sec. III-A); IMU or wheel odometry can replace the velocity source","none","none (pose-graph optimization stated as orthogonal and not used)","voxelized, downsampled local point cloud stored in a hash table with a maximum number of points per voxel; voxels beyond maximum range removed","odometry and local voxel point map; original point coordinates retained within voxels (no centroid snapping)","CPU; faster than sensor frame rate on all presented datasets (abstract; Sec. V); KITTI-raw average 38 Hz with deskewing and 51 Hz without (Table V); computing hardware not stated and detailed runtime deferred to the project page (Sec. IV-A)","https:\u002F\u002Fgithub.com\u002FPRBonn\u002Fkiss-icp","MIT (LICENSE file checked)",[52,56],{"relation":53,"title":54,"doi_or_url":55},"preprint","KISS-ICP (arXiv v2)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2209.15397",{"relation":57,"title":58,"doi_or_url":49},"code_release","PRBonn\u002Fkiss-icp",{"id":5,"kind":60,"shortName":7,"title":61,"authors":62,"year":9,"venue":69,"venueType":70,"publisher":71,"volumeIssuePages":72,"doi":73,"arxivId":74,"url":75,"firstPublicDate":76,"publicationStatus":16,"metadataStatus":77,"fulltextStatus":15,"era":10,"classicReason":78,"codeUrl":49,"cluster":11,"topics":79,"mdpi":80,"verification":81,"label":6,"fulltextRoute":82,"versionRead":83,"addedByCensus":80},"method","KISS-ICP: In Defense of Point-to-Point ICP – Simple, Accurate, and Robust Registration If Done the Right Way",[63,64,65,66,67,68],"Ignacio Vizzo","Tiziano Guadagnino","Benedikt Mersch","Louis Wiesmann","Jens Behley","Cyrill Stachniss","IEEE Robotics and Automation Letters","journal","IEEE","8(2):1029-1036","10.1109\u002Flra.2023.3236571","2209.15397","https:\u002F\u002Fapi.crossref.org\u002Fworks\u002F10.1109\u002FLRA.2023.3236571","2022-09-30","metadata_verified","not_applicable",[11],false,"confirmed","arXiv","arXiv v2 (2209.15397v2, 2023-07-07, posted after RA-L publication); IEEE version of record not opened",[85],{"category":86,"model":18,"canonical":87,"role":88,"dataset":89,"specs":90,"locator":91},"lidar",null,"dataset sensor","handheld Livox data cited as [17] (Loam_livox paper; Fig. 1 only)","handheld Livox LiDAR, model not given; non-repetitive shooting pattern different from rotating mechanical LiDARs","Fig. 1 caption",[],{"totalRows":94,"groupCount":95,"groups":96,"others":899},242,44,[97,389,546,770],{"slug":98,"group":99,"sourceId":100,"sourceLabel":101,"table":102,"selfRows":103,"metrics":104,"seqs":113,"entrants":166,"cells":180,"outcomes":382,"locators":384,"hardware":385,"wordings":386,"notes":387},"madicp2024-table-ii","madicp2024:Table II","madicp2024","Ferrari et al., 2024","Table II",19,[105,109,111],{"label":106,"unit":107,"statistic":108,"alignment":18},"RPE [%] (segments 100-800 m)","%","mean",{"label":110,"unit":107,"statistic":108,"alignment":18},"RPE [%] (segments 10-80 m)",{"label":112,"unit":107,"statistic":108,"alignment":18},"RPE [%] (segments mixed)",[114,118,122,126,128,131,133,135,138,140,143,145,148,150,153,154,158,160,162],{"dataset":115,"sequence":116,"environment":117},"KITTI","KITTI avg (Seq. 00-10)","car, urban and highway (Velodyne HDL-64)",{"dataset":119,"sequence":120,"environment":121},"MulRan","MulRan avg (12 sequences)","car, urban (Ouster OS1-64)",{"dataset":123,"sequence":124,"environment":125},"Newer College NC0 (OS0-128)","cat. easy","handheld, catacombs",{"dataset":123,"sequence":127,"environment":125},"cat. med.",{"dataset":123,"sequence":129,"environment":130},"cloister","handheld campus",{"dataset":123,"sequence":132,"environment":130},"m. easy",{"dataset":123,"sequence":134,"environment":130},"m. med.",{"dataset":123,"sequence":136,"environment":137},"quad easy","handheld campus quad",{"dataset":123,"sequence":139,"environment":137},"quad med.",{"dataset":123,"sequence":141,"environment":142},"stairs","handheld, indoor stairwell",{"dataset":123,"sequence":144,"environment":39},"avg",{"dataset":146,"sequence":147,"environment":130},"Newer College NC1 (OS1-64)","short",{"dataset":146,"sequence":149,"environment":130},"long",{"dataset":146,"sequence":151,"environment":152},"parkland","handheld park",{"dataset":146,"sequence":144,"environment":39},{"dataset":155,"sequence":156,"environment":157},"Hilti 2021 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(red in Table II: wrong registration produced local-map duplicates and a trajectory discontinuity; excluded from averages)",[102],[],[],[388],"KITTI benchmark RPE (%); segments 100-800 m for KITTI, MulRan and NC1, 10-80 m for NC0 and Hilti; averages exclude failures; per-sequence KITTI 00-10 and MulRan rows omitted (averages kept); values identical in arXiv v1 and version of record",{"slug":390,"group":391,"sourceId":5,"sourceLabel":6,"table":392,"selfRows":353,"metrics":393,"seqs":404,"entrants":414,"cells":424,"outcomes":540,"locators":541,"hardware":542,"wordings":543,"notes":544},"kissicp2023-table-iii","kissicp2023:Table III","Table III",[394,396,398,401],{"label":395,"unit":107,"statistic":108,"alignment":18},"Avg. tra. (KITTI relative translational error)",{"label":397,"unit":18,"statistic":108,"alignment":18},"Avg. rot. (KITTI relative rotational error; unit not stated)",{"label":399,"unit":400,"statistic":18,"alignment":18},"ATE tra. [m]","m",{"label":402,"unit":403,"statistic":18,"alignment":18},"ATE rot. [rad]","rad",[405,408,410,412],{"dataset":119,"sequence":406,"environment":407},"KAIST","urban driving",{"dataset":119,"sequence":409,"environment":407},"DCC",{"dataset":119,"sequence":411,"environment":407},"Riverside",{"dataset":119,"sequence":413,"environment":407},"Sejong* (asterisk not explained in the text read; SuMa not listed)",[415,417,420,422],{"name":416,"methodId":176,"linkable":169,"proposed":80,"self":80},"MULLS [21]",{"name":418,"methodId":419,"linkable":169,"proposed":80,"self":80},"SuMa [1]","suma2018",{"name":421,"methodId":173,"linkable":169,"proposed":80,"self":80},"F-LOAM [33]",{"name":423,"methodId":5,"linkable":169,"proposed":169,"self":169},"Ours (KISS-ICP)",[425,427,428,430,432,434,436,438,440,442,444,446,448,450,452,454,456,458,460,462,464,466,468,470,471,473,475,477,479,481,483,485,487,489,491,493,495,497,498,500,502,504,506,508,510,512,513,515,517,519,521,523,525,527,529,531,532,534,536,538],[182,182,182,426,184,182,184,184,182],2.94,[182,186,182,301,184,182,184,184,182],[182,189,182,429,184,182,184,184,182],37.24,[182,192,182,431,184,182,184,184,182],0.11,[186,182,182,433,184,182,184,184,182],5.59,[186,186,182,435,184,182,184,184,182],1.73,[186,189,182,437,184,182,184,184,182],43.61,[186,192,182,439,184,182,184,184,182],0.14,[189,182,182,441,184,182,184,184,182],3.43,[189,186,182,443,184,182,184,184,182],0.99,[189,189,182,445,184,182,184,184,182],46.17,[189,192,182,447,184,182,184,184,182],0.15,[192,182,182,449,184,182,184,184,182],2.28,[192,186,182,451,184,182,184,184,182],0.68,[192,189,182,453,184,182,184,184,182],17.4,[192,192,182,455,184,182,184,184,182],0.06,[182,182,186,457,184,182,184,184,182],2.96,[182,186,186,459,184,182,184,184,182],0.98,[182,189,186,461,184,182,184,184,182],38.35,[182,192,186,463,184,182,184,184,182],0.12,[186,182,186,465,184,182,184,184,182],5.2,[186,186,186,467,184,182,184,184,182],1.71,[186,189,186,469,184,182,184,184,182],36.22,[186,192,186,431,184,182,184,184,182],[189,182,186,472,184,182,184,184,182],3.83,[189,186,186,474,184,182,184,184,182],1.14,[189,189,186,476,184,182,184,184,182],42.7,[189,192,186,478,184,182,184,184,182],0.13,[192,182,186,480,184,182,184,184,182],2.34,[192,186,186,482,184,182,184,184,182],0.64,[192,189,186,484,184,182,184,184,182],15.16,[192,192,186,486,184,182,184,184,182],0.05,[182,182,189,488,184,182,184,184,182],5.42,[182,186,189,490,184,182,184,184,182],2.21,[182,189,189,492,184,182,184,184,182],91.16,[182,192,189,494,184,182,184,184,182],0.16,[186,182,189,496,184,182,184,184,182],13.86,[186,186,189,356,184,182,184,184,182],[186,189,189,499,184,182,184,184,182],227.24,[186,192,189,501,184,182,184,184,182],0.38,[189,182,189,503,184,182,184,184,182],5.47,[189,186,189,505,184,182,184,184,182],1.18,[189,189,189,507,184,182,184,184,182],138.09,[189,192,189,509,184,182,184,184,182],0.22,[192,182,189,511,184,182,184,184,182],2.89,[192,186,189,482,184,182,184,184,182],[192,189,189,514,184,182,184,184,182],49.02,[192,192,189,516,184,182,184,184,182],0.08,[182,182,192,518,184,182,184,184,182],5.93,[182,186,192,520,184,182,184,184,182],0.84,[182,189,192,522,184,182,184,184,182],2151,[182,192,192,524,184,182,184,184,182],0.49,[189,182,192,526,184,182,184,184,182],7.87,[189,186,192,528,184,182,184,184,182],1.2,[189,189,192,530,184,182,184,184,182],3448.97,[189,192,192,183,184,182,184,184,182],[192,182,192,533,184,182,184,184,182],4.69,[192,186,192,535,184,182,184,184,182],0.7,[192,189,192,537,184,182,184,184,182],1369.54,[192,192,192,539,184,182,184,184,182],0.33,[],[392],[],[],[545],"MulRan; values are averages over the three runs per sequence; CT-ICP not evaluated because it lacks MulRan support",{"slug":547,"group":548,"sourceId":549,"sourceLabel":550,"table":102,"selfRows":333,"metrics":551,"seqs":557,"entrants":576,"cells":591,"outcomes":764,"locators":765,"hardware":766,"wordings":767,"notes":768},"kinematicicp2025-table-ii","kinematicicp2025:Table II","kinematicicp2025","Guadagnino et al., 2025b",[552,554],{"label":553,"unit":107,"statistic":108,"alignment":18},"RPE [%] (KITTI metric, 1-100 m segments)",{"label":555,"unit":400,"statistic":556,"alignment":18},"ATE [m] (root mean squared absolute translation error after alignment)","RMSE",[558,562,564,566,569,572,574],{"dataset":559,"sequence":560,"environment":561},"authors' warehouse and campus sequences","Campus 0","flat campus pavement, Clearpath Husky; reference Leica Nova MS60 total station",{"dataset":559,"sequence":563,"environment":561},"Campus 1",{"dataset":559,"sequence":565,"environment":561},"Campus 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