[{"data":1,"prerenderedAt":235},["ShallowReactive",2],{"method-koide2024smallgicp":3},{"method":4,"reference":46,"equipment":65,"figures":66,"results":67},{"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":24,"sensors":28,"platform":31,"estimator":32,"association":33,"timeModel":34,"deskew":18,"loopClosure":35,"globalOptimization":35,"mapRepresentation":36,"prior":37,"outputGeometry":38,"compute":39,"codeUrl":40,"codeLicense":41,"relatedVersions":42},"koide2024smallgicp","Koide, 2024","small_gicp","small_gicp: Efficient and parallel algorithms for point cloud registration",2024,"recent","C02","registration_component","small_gicp 是僅需標頭檔的 C++ 點雲精配準函式庫，平行化下採樣、最近鄰搜尋、局部特徵估計與配準整條流程，以減少 PCL 與 Open3D 僅部分多執行緒所造成的瓶頸。它提供點對點、點對平面與 GICP 誤差、穩健核、Gauss-Newton 與 Levenberg-Marquardt 最佳化器，以及 KdTree、iVox 與高斯體素地圖，並有 Python 綁定。","Header-only, fully parallel C++ library (with Python bindings) implementing ICP, point-to-plane, GICP and voxel-based GICP-style registration with modular templates.","full_text_reviewed","peer_reviewed_published","background","not_reported",[20],"public_benchmark",[22,23],"up to 2x single-thread speed gain and better multi-core scalability (Statement of need)","modular template design eases replacing cost functions and search methods (Statement of need)",[25,26,27],"future work: faster neighbour search such as projective search (Future work)","small_vgicp results differ slightly from fast_vgicp and the author states the difference needs investigation (BENCHMARK.md, Accuracy)","(reviewer observation) benchmarks are speed-focused and use only KITTI 00; accuracy on engineering data is not reported",[29,30],"3D LiDAR","range cameras",[],"Gauss-Newton or Levenberg-Marquardt least squares with robust kernels (Functionalities)","KdTree, linear iVox and Gaussian voxelmap (incremental insertion, LRU deletion); point-to-point, point-to-plane and GICP error factors (Functionalities)","not_applicable (library)","none","KdTree, iVox, Gaussian voxelmap","initial guess required (fine\u002Flocal registration)","rigid transformation","single-threaded GICP about 2.4x faster than pcl::GICP (BENCHMARK.md adds about 1.9x faster than fast_gicp::GICP); 6-thread voxel downsampling about 3.2x and single-thread about 1.3x faster than pcl::VoxelGrid; multi-threaded KdTree construction up to 6x faster than nanoflann (JOSS); all on KITTI 00 per BENCHMARK.md; benchmark machine not stated","https:\u002F\u002Fgithub.com\u002Fkoide3\u002Fsmall_gicp","MIT (LICENSE file checked); paper text CC BY 4.0",[43],{"relation":44,"title":45,"doi_or_url":40},"code_release","small_gicp repository (successor of fast_gicp per README)",{"id":5,"kind":47,"shortName":7,"title":8,"authors":48,"year":9,"venue":50,"venueType":51,"publisher":52,"volumeIssuePages":53,"doi":54,"arxivId":55,"url":56,"firstPublicDate":57,"publicationStatus":16,"metadataStatus":58,"fulltextStatus":15,"era":10,"classicReason":59,"codeUrl":40,"cluster":11,"topics":60,"mdpi":61,"verification":62,"label":6,"fulltextRoute":63,"versionRead":64,"addedByCensus":61},"software",[49],"Kenji Koide","Journal of Open Source Software","journal","The Open Journal","9(100):6948","10.21105\u002Fjoss.06948",null,"https:\u002F\u002Fjoss.theoj.org\u002Fpapers\u002F10.21105\u002Fjoss.06948.pdf","2024-08-10","metadata_verified","not_applicable",[11],false,"confirmed","publisher OA","version of record, JOSS 9(100):6948 PDF (3 pages), plus BENCHMARK.md in the repository (master branch) that the paper points to for details",[],[],{"totalRows":68,"groupCount":69,"groups":70,"others":234},22,4,[71,148,189,211],{"slug":72,"group":73,"sourceId":5,"sourceLabel":6,"table":74,"selfRows":75,"metrics":76,"seqs":94,"entrants":99,"cells":108,"outcomes":141,"locators":142,"hardware":144,"wordings":145,"notes":146},"koide2024smallgicp-text-benchmark-md-accuracy","koide2024smallgicp:Text BENCHMARK.md Accuracy","Text BENCHMARK.md Accuracy",16,[77,80,82,84,86,88,90,92],{"label":78,"unit":79,"statistic":18,"alignment":18},"APE = 6.096 +- 3.056","not_stated",{"label":81,"unit":79,"statistic":18,"alignment":18},"RPE(100) = 1.211 +- 0.717",{"label":83,"unit":79,"statistic":18,"alignment":18},"RPE(400) = 6.057 +- 3.123",{"label":85,"unit":79,"statistic":18,"alignment":18},"RPE(800) = 10.364 +- 6.336",{"label":87,"unit":79,"statistic":18,"alignment":18},"APE = 5.956 +- 2.725",{"label":89,"unit":79,"statistic":18,"alignment":18},"RPE(100) = 1.315 +- 0.762",{"label":91,"unit":79,"statistic":18,"alignment":18},"RPE(400) = 6.849 +- 3.401",{"label":93,"unit":79,"statistic":18,"alignment":18},"RPE(800) = 10.396 +- 6.972",[95],{"dataset":96,"sequence":97,"environment":98},"KITTI odometry sequence 00","00","vehicle, urban driving",[100,102,104,106],{"name":7,"methodId":5,"linkable":101,"proposed":101,"self":101},true,{"name":103,"methodId":5,"linkable":101,"proposed":101,"self":101},"small_gicp (tbb)",{"name":105,"methodId":5,"linkable":101,"proposed":101,"self":101},"small_gicp (omp)",{"name":107,"methodId":5,"linkable":101,"proposed":101,"self":101},"small_vgicp",[109,113,116,119,122,123,124,125,126,127,128,129,130,132,135,138],[110,110,110,111,112,110,112,112,110],0,6.096,-1,[110,114,110,115,112,110,112,112,110],1,1.211,[110,117,110,118,112,110,112,112,110],2,6.057,[110,120,110,121,112,110,112,112,110],3,10.364,[114,110,110,111,112,110,112,112,110],[114,114,110,115,112,110,112,112,110],[114,117,110,118,112,110,112,112,110],[114,120,110,121,112,110,112,112,110],[117,110,110,111,112,110,112,112,110],[117,114,110,115,112,110,112,112,110],[117,117,110,118,112,110,112,112,110],[117,120,110,121,112,110,112,112,110],[120,69,110,131,112,110,112,112,110],5.956,[120,133,110,134,112,110,112,112,110],5,1.315,[120,136,110,137,112,110,112,112,110],6,6.849,[120,139,110,140,112,110,112,112,110],7,10.396,[],[143],"BENCHMARK.md, Accuracy",[],[],[147],"repository documentation linked from the paper (BENCHMARK.md, master branch, fetched 2026-09-25), not peer-reviewed text; odometry benchmark on KITTI 00; units and the meaning of '+-' and of the RPE window (100, 400, 800) are not stated; value is the number before '+-'",{"slug":149,"group":150,"sourceId":5,"sourceLabel":6,"table":151,"selfRows":69,"metrics":152,"seqs":162,"entrants":165,"cells":174,"outcomes":182,"locators":183,"hardware":185,"wordings":186,"notes":187},"koide2024smallgicp-text-benchmark-results","koide2024smallgicp:Text Benchmark results","Text Benchmark results",[153,156,158,160],{"label":154,"unit":155,"statistic":18,"alignment":59},"speed-up factor (approximately 1.3x faster)","x",{"label":157,"unit":155,"statistic":18,"alignment":59},"speed-up factor (approximately 3.2x faster)",{"label":159,"unit":155,"statistic":18,"alignment":59},"speed-up factor (up to 6x faster)",{"label":161,"unit":155,"statistic":18,"alignment":59},"speed-up factor (about 2.4x faster)",[163],{"dataset":164,"sequence":97,"environment":98},"KITTI odometry sequence 00 (per BENCHMARK.md)",[166,168,170,172],{"name":167,"methodId":5,"linkable":101,"proposed":101,"self":101},"small_gicp::voxelgrid_sampling (single-thread) vs pcl::VoxelGrid",{"name":169,"methodId":5,"linkable":101,"proposed":101,"self":101},"small_gicp::voxelgrid_sampling (6 threads) vs pcl::VoxelGrid",{"name":171,"methodId":5,"linkable":101,"proposed":101,"self":101},"small_gicp::KdTree multi-threaded construction vs nanoflann",{"name":173,"methodId":5,"linkable":101,"proposed":101,"self":101},"small_gicp::GICP (single-thread) vs pcl::GICP",[175,177,179,180],[110,110,110,176,112,110,112,112,110],1.3,[114,114,110,178,112,110,112,112,110],3.2,[117,117,110,136,112,110,112,112,110],[120,120,110,181,112,110,112,112,110],2.4,[],[184],"Benchmark results",[],[],[188],"speed ratios stated in the JOSS paper; details deferred to BENCHMARK.md (KITTI sequence 00); benchmark machine not stated",{"slug":190,"group":191,"sourceId":5,"sourceLabel":6,"table":192,"selfRows":114,"metrics":193,"seqs":196,"entrants":198,"cells":201,"outcomes":204,"locators":205,"hardware":207,"wordings":208,"notes":209},"koide2024smallgicp-text-benchmark-md-odometry-estimation","koide2024smallgicp:Text BENCHMARK.md Odometry estimation","Text BENCHMARK.md Odometry estimation",[194],{"label":195,"unit":155,"statistic":18,"alignment":59},"speed-up factor (about 1.9x faster than fast_gicp::GICP, single-thread)",[197],{"dataset":96,"sequence":97,"environment":98},[199],{"name":200,"methodId":5,"linkable":101,"proposed":101,"self":101},"small_gicp::GICP (single-thread) vs fast_gicp::GICP",[202],[110,110,110,203,112,110,112,112,110],1.9,[],[206],"BENCHMARK.md, Odometry estimation",[],[],[210],"repository documentation linked from the paper (master branch, fetched 2026-09-25); not peer-reviewed text",{"slug":212,"group":213,"sourceId":5,"sourceLabel":6,"table":214,"selfRows":114,"metrics":215,"seqs":218,"entrants":222,"cells":225,"outcomes":227,"locators":228,"hardware":230,"wordings":231,"notes":232},"koide2024smallgicp-text-statement-of-need","koide2024smallgicp:Text Statement of need","Text Statement of need",[216],{"label":217,"unit":155,"statistic":18,"alignment":59},"speed gain (up to 2x in single-threaded scenarios)",[219],{"dataset":220,"sequence":18,"environment":221},"not stated in the Statement of need (BENCHMARK.md benchmarks use KITTI 00)","not stated",[223],{"name":224,"methodId":5,"linkable":101,"proposed":101,"self":101},"small_gicp pipeline (single-thread) vs existing libraries",[226],[110,110,110,117,112,110,112,112,110],[],[229],"Statement of need",[],[],[233],"single-thread speed gain claimed in the Statement of need of the JOSS paper; no dataset, baseline library or machine named for this figure",[],1790510665815]