[{"data":1,"prerenderedAt":359},["ShallowReactive",2],{"method-censi2008_plicp":3},{"method":4,"reference":57,"equipment":77,"figures":95,"results":96},{"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":31,"platform":33,"estimator":35,"association":36,"timeModel":37,"deskew":38,"loopClosure":38,"globalOptimization":38,"mapRepresentation":39,"prior":40,"outputGeometry":41,"compute":42,"codeUrl":43,"codeLicense":44,"relatedVersions":45},"censi2008_plicp","Censi, 2008","PL-ICP and CSM (laser_scan_matcher)","An ICP variant using a point-to-line metric",2008,"classic","C01","registration_component","PL-ICP 是採用點到線（point-to-line）度量的 2D ICP 變體：參考掃描以相鄰點連成折線，目前掃描的每個點對應到最近兩點形成的線段，並以作者推導的精確閉式解最小化點到線距離。作者引用 Pottmann 等的結果說明，點到線度量在零殘差且初值良好時具二次收斂；並證明當參考曲面為折線時，演算法會在有限步內收斂到固定點或循環。附錄另提出利用射線角度排序、提前停止與跳躍表的快速對應搜尋。作者的 CSM 函式庫就是 ROS laser_scan_matcher 增量式 2D 雷射里程計所用的匹配核心。","2D ICP variant with a point-to-line metric solved in exact closed form each iteration, giving quadratic convergence near the solution and finite-step termination, with a fast correspondence search; implemented in the CSM library used by ROS laser_scan_matcher.","full_text_reviewed","peer_reviewed_published","background","未在營建場域驗證；資料為機器人輪椅搭載 SICK 的室內記錄。它是 2D 掃描匹配元件，透過 ROS laser_scan_matcher 可在缺少輪式里程計的平台上提供增量式雷射里程計；在長走廊等沿牆方向缺少約束的環境中，點到線度量對沿牆平移的約束很弱，這項風險論文未評估（推論）。",[20],"controlled_experiment",[22,23,24,25],"In the reproduced artificial-error experiment PLICP put 99.85% of trials in the \u003C 0.001 bucket for Experiment 1, versus 81.27% for MBICP, 83.31% for IDC and 57.78% for ICP (Fig. 3).","On average 7.2 iterations and 0.0018 s per matching, versus 31.2 iterations and 0.076 s for MBICP (Sec. V.B).","GPM coarse alignment followed by PLICP gave the best accuracy for 99.79% of trials in Experiment 6 (Sec. V.A).","Needs fewer parameters than vanilla ICP: no convergence thresholds and no search bounds (Sec. VI).",[27,28,29,30],"Less robust to large rotational initial errors: in Experiment 6 (up to 45 deg) 24.81% of trials ended with errors above 0.05 (Fig. 3; Sec. V.A).","In the test each scan is matched against itself, which the author calls unrealistic because real scans overlap only partially (Sec. V.A).","Absolute timing comparisons depend heavily on the implementation (Sec. V.B).","The theoretical convergence results hold for idealized algorithms without outlier rejection (Sec. III).",[32],"2D laser range finder (SICK, 360 rays over 180 deg in the test log)",[34],"wheeled UGV (robotic wheelchair; log from Minguez et al.)","iterative point-to-segment matching; each iteration minimizes the point-to-line error in exact closed form via Lagrange multipliers (a fourth-order polynomial in lambda); trimming rejects outliers (Sec. II; App. I)","each transformed point is matched to the segment between its two closest points in the reference scan, which is treated as a polyline; fast search exploits radial ordering, early stopping and precomputed jump tables (Sec. II; App. II)","not_applicable (pairwise scan matching)","not_applicable","not_applicable (reference scan as polyline)","initial guess required (odometry); less robust to large rotational initial errors","2D rigid transform (t, theta) between two scans","Pentium IV 1.8 GHz: on average 7.2 iterations and 0.0018 s (539 Hz) per matching for 360-ray scans (Sec. V.B)","https:\u002F\u002Fgithub.com\u002FAndreaCensi\u002Fcsm","LGPL (csm package.xml); the laser_scan_matcher package.xml states CSM is LGPLv3 and the wrapper BSD",[46,49,53],{"relation":47,"title":48,"doi_or_url":43},"code_release","CSM (C scan matcher) stated in the paper at purl.org\u002Fcensi\u002F2007\u002Fcsm; GitHub AndreaCensi\u002Fcsm",{"relation":50,"title":51,"doi_or_url":52},"software_using_this_method","ROS laser_scan_matcher (CCNYRoboticsLab\u002Fscan_tools), incremental scan matcher built on CSM","https:\u002F\u002Fgithub.com\u002FCCNYRoboticsLab\u002Fscan_tools",{"relation":54,"title":55,"doi_or_url":56},"repository_record","Caltech authors repository record listed by OpenAlex (not read)","https:\u002F\u002Fauthors.library.caltech.edu\u002F18274\u002F",{"id":5,"kind":58,"shortName":7,"title":8,"authors":59,"year":9,"venue":61,"venueType":62,"publisher":63,"volumeIssuePages":64,"doi":65,"arxivId":66,"url":67,"firstPublicDate":68,"publicationStatus":16,"metadataStatus":69,"fulltextStatus":15,"era":10,"classicReason":70,"codeUrl":43,"cluster":11,"topics":71,"mdpi":72,"verification":73,"label":6,"fulltextRoute":74,"versionRead":75,"addedByCensus":76},"component",[60],"Andrea Censi","2008 IEEE International Conference on Robotics and Automation (ICRA), Pasadena, CA","conference","IEEE","pp. 19-25","10.1109\u002Frobot.2008.4543181",null,"https:\u002F\u002Fdoi.org\u002F10.1109\u002FROBOT.2008.4543181","2008-05","metadata_verified","reproducible baseline and principle reused: exact closed-form point-to-line ICP in 2D, the planar counterpart of point-to-plane registration; its CSM library is the matching engine of ROS laser_scan_matcher (package.xml checked). Borderline under the component rule because it is a pairwise registration method rather than a SLAM system.",[11],false,"confirmed","NTU institutional (curl)","IEEE Xplore version of record PDF (ICRA 2008, pp. 19-25, 7 pp.)",true,[78,85,89],{"category":79,"model":80,"canonical":80,"role":81,"dataset":82,"specs":83,"locator":84},"lidar","Sick range-sensor","dataset sensor","Minguez et al. (2006) scan log","360 rays over a 180 deg field of view","Sec. V",{"category":86,"model":87,"canonical":87,"role":81,"dataset":82,"specs":88,"locator":84},"platform","robotic wheel-chair","scans taken about every 0.3 m with considerable odometry slip",{"category":90,"model":91,"canonical":91,"role":92,"dataset":66,"specs":93,"locator":94},"compute","Pentium IV 1.8GhZ","compute for runtime","not_reported","Sec. I; Sec. V.B",[],{"totalRows":97,"groupCount":98,"groups":99,"others":358},28,3,[100,277,316],{"slug":101,"group":102,"sourceId":5,"sourceLabel":6,"table":103,"selfRows":104,"metrics":105,"seqs":112,"entrants":127,"cells":141,"outcomes":270,"locators":271,"hardware":273,"wordings":274,"notes":275},"censi2008-plicp-fig-3-table","censi2008_plicp:Fig. 3 table","Fig. 3 table",24,[106,110],{"label":107,"unit":108,"statistic":93,"alignment":109},"% of trials with error \u003C 0.001 (m, rad)","%","none",{"label":111,"unit":108,"statistic":93,"alignment":109},"% of trials with error > 0.05 (m, rad)",[113,117,119,121,123,125],{"dataset":114,"sequence":115,"environment":116},"Minguez et al. (2006) wheelchair SICK log (778 scans)","Experiment 1 (0.05 m, 0.05 m, 2 deg)","indoor (log environment not described)",{"dataset":114,"sequence":118,"environment":116},"Experiment 2 (0.10 m, 0.10 m, 4 deg)",{"dataset":114,"sequence":120,"environment":116},"Experiment 3 (0.15 m, 0.15 m, 8.6 deg)",{"dataset":114,"sequence":122,"environment":116},"Experiment 4 (0.20 m, 0.20 m, 17.2 deg)",{"dataset":114,"sequence":124,"environment":116},"Experiment 5 (0.20 m, 0.20 m, 32 deg)",{"dataset":114,"sequence":126,"environment":116},"Experiment 6 (0.20 m, 0.20 m, 45 deg)",[128,130,132,135,137,139],{"name":129,"methodId":66,"linkable":72,"proposed":72,"self":72},"MBICP",{"name":131,"methodId":66,"linkable":72,"proposed":72,"self":72},"IDC",{"name":133,"methodId":134,"linkable":76,"proposed":72,"self":72},"ICP","besl1992icp",{"name":136,"methodId":5,"linkable":76,"proposed":76,"self":76},"PLICP",{"name":138,"methodId":66,"linkable":72,"proposed":72,"self":72},"GPM",{"name":140,"methodId":5,"linkable":76,"proposed":76,"self":76},"GPM o PLICP",[142,146,148,150,151,154,155,157,158,161,163,166,167,169,170,172,173,175,176,178,180,182,184,185,186,188,189,191,193,194,196,198,200,202,204,206,207,209,210,212,214,216,218,220,222,224,226,228,230,232,234,236,238,240,242,244,246,247,248,249,250,252,254,256,258,260,262,264,266,267,268,269],[143,143,143,144,145,143,145,145,143],0,81.27,-1,[143,147,143,143,145,143,145,145,143],1,[147,143,143,149,145,143,145,145,143],83.31,[147,147,143,143,145,143,145,145,143],[152,143,143,153,145,143,145,145,143],2,57.78,[152,147,143,143,145,143,145,145,143],[98,143,143,156,145,143,145,145,143],99.85,[98,147,143,143,145,143,145,145,143],[159,143,143,160,145,143,145,145,143],4,1.86,[159,147,143,162,145,143,145,145,143],0.01,[164,143,143,165,145,143,145,145,143],5,99.98,[164,147,143,143,145,143,145,145,143],[143,143,147,168,145,143,145,145,143],80.97,[143,147,147,143,145,143,145,145,143],[147,143,147,171,145,143,145,145,143],83.12,[147,147,147,143,145,143,145,145,143],[152,143,147,174,145,143,145,145,143],56.62,[152,147,147,143,145,143,145,145,143],[98,143,147,177,145,143,145,145,143],99.71,[98,147,147,179,145,143,145,145,143],0.02,[159,143,147,181,145,143,145,145,143],1.3,[159,147,147,183,145,143,145,145,143],2.17,[164,143,147,165,145,143,145,145,143],[164,147,147,143,145,143,145,145,143],[143,143,152,187,145,143,145,145,143],80.84,[143,147,152,143,145,143,145,145,143],[147,143,152,190,145,143,145,145,143],82.95,[147,147,152,192,145,143,145,145,143],0.03,[152,143,152,174,145,143,145,145,143],[152,147,152,195,145,143,145,145,143],0.002,[98,143,152,197,145,143,145,145,143],99.51,[98,147,152,199,145,143,145,145,143],0.08,[159,143,152,201,145,143,145,145,143],0.91,[159,147,152,203,145,143,145,145,143],5.71,[164,143,152,205,145,143,145,145,143],99.95,[164,147,152,143,145,143,145,145,143],[143,143,98,208,145,143,145,145,143],81.28,[143,147,98,143,145,143,145,145,143],[147,143,98,211,145,143,145,145,143],81.96,[147,147,98,213,145,143,145,145,143],0.44,[152,143,98,215,145,143,145,145,143],56.3,[152,147,98,217,145,143,145,145,143],0.1,[98,143,98,219,145,143,145,145,143],98.43,[98,147,98,221,145,143,145,145,143],0.92,[159,143,98,223,145,143,145,145,143],0.61,[159,147,98,225,145,143,145,145,143],9.3,[164,143,98,227,145,143,145,145,143],99.79,[164,147,98,229,145,143,145,145,143],0.11,[143,143,159,231,145,143,145,145,143],80.92,[143,147,159,233,145,143,145,145,143],0.28,[147,143,159,235,145,143,145,145,143],79.54,[147,147,159,237,145,143,145,145,143],3.05,[152,143,159,239,145,143,145,145,143],54,[152,147,159,241,145,143,145,145,143],2.85,[98,143,159,243,145,143,145,145,143],84.48,[98,147,159,245,145,143,145,145,143],14.11,[159,143,159,223,145,143,145,145,143],[159,147,159,225,145,143,145,145,143],[164,143,159,227,145,143,145,145,143],[164,147,159,229,145,143,145,145,143],[143,143,164,251,145,143,145,145,143],80.38,[143,147,164,253,145,143,145,145,143],0.75,[147,143,164,255,145,143,145,145,143],74.94,[147,147,164,257,145,143,145,145,143],7.32,[152,143,164,259,145,143,145,145,143],52.18,[152,147,164,261,145,143,145,145,143],5.78,[98,143,164,263,145,143,145,145,143],73.46,[98,147,164,265,145,143,145,145,143],24.81,[159,143,164,223,145,143,145,145,143],[159,147,164,225,145,143,145,145,143],[164,143,164,227,145,143,145,145,143],[164,147,164,229,145,143,145,145,143],[],[272],"Fig. 3 (table)",[],[],[276],"Minguez et al. (2006) artificial-error experiment: each of 778 scans matched against a copy of itself displaced by a uniform random error up to the listed bound, 100 trials per scan; errors bucketed by the maximum absolute component in m and rad; MBICP, IDC and ICP columns copied from Minguez et al. Column order follows the table header (MBICP, IDC, ICP); the text lists them as MBICP, ICP, IDC. Only the '\u003C 0.001' and '> 0.05' buckets are extracted.",{"slug":278,"group":279,"sourceId":5,"sourceLabel":6,"table":280,"selfRows":152,"metrics":281,"seqs":289,"entrants":294,"cells":299,"outcomes":308,"locators":309,"hardware":311,"wordings":313,"notes":314},"censi2008-plicp-text-app-ii-b-table","censi2008_plicp:Text App. II.B table","Text App. II.B table",[282,286],{"label":283,"unit":284,"statistic":285,"alignment":109},"avg. comparisons per ray per iteration","comparisons","mean",{"label":287,"unit":288,"statistic":285,"alignment":109},"iterations per second","Hz",[290],{"dataset":291,"sequence":292,"environment":293},"Minguez et al. (2006) wheelchair SICK log","full log","indoor",[295,297],{"name":296,"methodId":5,"linkable":76,"proposed":76,"self":76},"smart correspondence search",{"name":298,"methodId":66,"linkable":72,"proposed":72,"self":72},"naive correspondence search",[300,302,304,306],[143,143,143,301,145,143,143,145,143],6,[143,147,143,303,145,143,143,145,143],539,[147,143,143,305,145,143,143,145,143],124.9,[147,147,143,307,145,143,143,145,143],93,[],[310],"App. II.B",[312],"Pentium IV 1.8 GHz",[],[315],"Correspondence search cost on the same log; naive search uses max |t| = 0.5 m and max |theta| = 25 deg",{"slug":317,"group":318,"sourceId":5,"sourceLabel":6,"table":319,"selfRows":152,"metrics":320,"seqs":327,"entrants":329,"cells":334,"outcomes":351,"locators":352,"hardware":354,"wordings":355,"notes":356},"censi2008-plicp-text-sec-v-b-table","censi2008_plicp:Text Sec. V.B table","Text Sec. V.B table",[321,324],{"label":322,"unit":323,"statistic":285,"alignment":109},"avg. iterations","iterations",{"label":325,"unit":326,"statistic":285,"alignment":109},"avg. execution time","s",[328],{"dataset":291,"sequence":292,"environment":293},[330,331,332,333],{"name":129,"methodId":66,"linkable":72,"proposed":72,"self":72},{"name":133,"methodId":134,"linkable":76,"proposed":72,"self":72},{"name":131,"methodId":66,"linkable":72,"proposed":72,"self":72},{"name":136,"methodId":5,"linkable":76,"proposed":76,"self":76},[335,337,339,341,343,345,347,349],[143,143,143,336,145,143,143,145,143],31.2,[143,147,143,338,145,143,143,145,143],0.076,[147,143,143,340,145,143,143,145,143],34.7,[147,147,143,342,145,143,143,145,143],0.083,[152,143,143,344,145,143,143,145,143],30.4,[152,147,143,346,145,143,143,145,143],0.24,[98,143,143,348,145,143,143,145,143],7.2,[98,147,143,350,145,143,143,145,143],0.0018,[],[353],"Sec. V.B",[312],[],[357],"Average iterations and execution time per scan matching on the same log; MBICP, ICP and IDC values copied from Minguez et al.; the author cautions that absolute timing depends heavily on implementation",[],1790510661898]