[{"data":1,"prerenderedAt":286},["ShallowReactive",2],{"method-borrmann2014thermalmapping":3},{"method":4,"reference":51,"equipment":76,"figures":106,"results":107},{"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":36,"estimator":38,"association":39,"timeModel":40,"deskew":41,"loopClosure":42,"globalOptimization":43,"mapRepresentation":44,"prior":45,"outputGeometry":46,"compute":47,"codeUrl":48,"codeLicense":49,"relatedVersions":50},"borrmann2014thermalmapping","Borrmann et al., 2014","Irma3D automated thermal 3D mapping","A mobile robot based system for fully automated thermal 3D mapping",2014,"classic","C11b","downstream_engineering_task","作者提出由機器人 Irma3D 全自動建立建築物熱影像三維模型的系統。平台以 Riegl VZ-400 地面雷射掃描儀為主感測器，上方裝 optris PI160 熱像儀與網路攝影機，以停走方式在各站掃描，每站再以 3DTK 的 6D SLAM 配準成同一座標系。熱像儀以燈泡陣列板做內參與相對掃描儀的外參校正，並以沿光線檢查遮擋的方式把溫度與顏色指派給點雲。站點選擇結合二維 NBV 探索與房間偵測後的三維體素 NBV 規劃，以減少天花板、地板與家具後方的遺漏；最後以行進立方體重建網格、映射溫度場並自動標出熱源。","Autonomous robot (Irma3D) that builds thermal 3D building models from stop-and-go Riegl VZ-400 scans registered by 3DTK 6D SLAM, with calibrated thermal and colour cameras, occlusion-aware projection, combined 2D and 3D next-best-view planning and marching-cubes reconstruction with heat-source detection.","full_text_reviewed","peer_reviewed_published","background","應用於既有建築的熱能檢測與節能改善，屬竣工建築的自動化掃描；三維幾何來自停走式地面雷射掃描與 6D SLAM 配準，論文本身未評估配準或幾何精度，只引用先前研究的定位誤差（Sec. 6）。",[20],"completed_building",[22,23,24,25],"Complete autonomous pipeline from exploration and data acquisition to a thermal 3D model and heat-source detection (Sec. 7)","3D NBV planning left no unseen voxels visible from any candidate position in room 1, while 2D-only exploration left 703 (Table 2; Sec. 5)","Room-based switching between 2D and 3D planning keeps memory and computation low (Sec. 4)","Cited prior work reports positional error below 4 cm for the 6D SLAM registration even in large outdoor environments (Sec. 6)",[27,28,29,30],"Each scan takes 3 min 15 s, so the number of scanning positions must be minimized (Sec. 2.2, 5)","Reconstruction precision is insufficient for furniture, monitors and people; outliers seen through windows reduce point density until removed by clustering (Sec. 6.3, 6.5)","Low thermal-camera resolution (160 x 120) makes calibration inaccuracies matter and causes projection errors at edges (Sec. 3.2.4)","Automatic interpretation of thermal flaws is still open; the work is fundamental research, not a ready product (Sec. 7)",[32,33,34,35],"terrestrial 3D laser scanner (Riegl VZ-400)","thermal camera (optris PI160)","colour webcam (Logitech QuickCam Pro 9000)","2D laser scanner (SICK LMS100) for obstacle avoidance",[37],"wheeled UGV (Irma3D on a Volksbot RT-3 chassis), stop-and-go scanning","Scan registration with 6D SLAM from 3DTK (The 3D Toolkit) for the scanner poses; GMapping under ROS for robot localization during exploration (Sec. 3.2.5, 4.1)","scan matching of stop-and-go 3D scans in 3DTK (details in cited work); calibration board detected in scans by RANSAC plane fitting plus ICP of a plane model (Algorithm 1)","stop-and-go: static 360 deg scans, images taken during a return rotation after each scan","not_applicable (static scans at each position)","as provided by 6D SLAM in 3DTK (not described in this paper)","6D SLAM registration in 3DTK (described in cited work)","registered 3D point cloud with reflectance, thermal and colour values; 0.2 m voxel model for 3D NBV planning; marching-cubes mesh with mapped temperature field (Sec. 4.3, 6)","none about the building (exploration from a blank map); room-detection height chosen manually (2.5 m)","thermal and colour 3D point cloud of a building floor and reconstructed thermal surface model with automatically detected heat sources","not_reported; reconstruction time 6.2 to 55.1 s depending on subdivision (Table 3)",null,"not_applicable",[],{"id":5,"kind":52,"shortName":7,"title":8,"authors":53,"year":9,"venue":61,"venueType":62,"publisher":63,"volumeIssuePages":64,"doi":65,"arxivId":48,"url":66,"firstPublicDate":67,"publicationStatus":16,"metadataStatus":68,"fulltextStatus":15,"era":10,"classicReason":69,"codeUrl":48,"cluster":11,"topics":70,"mdpi":71,"verification":72,"label":6,"fulltextRoute":73,"versionRead":74,"addedByCensus":75},"method",[54,55,56,57,58,59,60],"Dorit Borrmann","Andreas Nüchter","Marija Đakulović","Ivan Maurović","Ivan Petrović","Dinko Osmanković","Jasmin Velagić","Advanced Engineering Informatics","journal","Elsevier","28(4):425-440","10.1016\u002Fj.aei.2014.06.002","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS1474034614000408","2014-07-21","metadata_verified","necessary technical node for AEC: early fully autonomous building-scanning robot that registers stop-and-go terrestrial laser scans with 6D SLAM (3DTK), fuses thermal and colour images through calibrated projection with ray-traced occlusion checks, and plans 3D next-best-view positions, giving the reference system for robotic thermal 3D building models",[11],false,"corrected","NTU institutional (Chrome)","version of record, Advanced Engineering Informatics 28(4):425-440 (ScienceDirect HTML full text)",true,[77,83,88,92,96,101],{"category":78,"model":79,"canonical":79,"role":80,"dataset":48,"specs":81,"locator":82},"tls_scanner","Riegl VZ-400","method input","field of view 360 deg x 100 deg; stated accuracy 5 mm; head rotation used to carry the cameras","Sec. 3.1, Sec. 6",{"category":84,"model":85,"canonical":85,"role":80,"dataset":48,"specs":86,"locator":87},"thermal","optris PI160","160 x 120 px, thermal resolution 0.1 degC, 7.5 to 13 um, 120 Hz, accuracy 2 degC, FOV about 40 deg x 64 deg","Sec. 3.1",{"category":89,"model":90,"canonical":90,"role":80,"dataset":48,"specs":91,"locator":87},"camera","Logitech QuickCam Pro 9000","1600 x 1200 video resolution; 10 images per camera per 360 deg",{"category":93,"model":94,"canonical":94,"role":80,"dataset":48,"specs":95,"locator":87},"lidar","SICK LMS100","2D laser scanner at the front for obstacle avoidance",{"category":97,"model":98,"canonical":98,"role":80,"dataset":48,"specs":99,"locator":100},"platform","Irma3D (Volksbot RT-3 chassis)","mobile robot carrying scanner and cameras","Sec. 3.1; Fig. 2",{"category":102,"model":103,"canonical":103,"role":80,"dataset":48,"specs":104,"locator":105},"other","calibration board with 30 lamps (12 V, 4 mm bulbs) and chessboard pattern","500 mm x 570 mm board on a tripod for intrinsic and extrinsic thermal and colour camera calibration","Sec. 3.2.1-3.2.2",[],{"totalRows":108,"groupCount":109,"groups":110,"others":285},31,4,[111,179,219,262],{"slug":112,"group":113,"sourceId":5,"sourceLabel":6,"table":114,"selfRows":115,"metrics":116,"seqs":125,"entrants":134,"cells":139,"outcomes":173,"locators":174,"hardware":175,"wordings":176,"notes":177},"borrmann2014thermalmapping-table-2","borrmann2014thermalmapping:Table 2","Table 2",15,[117,121,123],{"label":118,"unit":119,"statistic":120,"alignment":49},"O (occupied voxels)","voxels","not_reported",{"label":122,"unit":119,"statistic":120,"alignment":49},"U (unseen voxels)",{"label":124,"unit":119,"statistic":120,"alignment":49},"UB (unseen voxels visible from the best next position)",[126,130,132],{"dataset":127,"sequence":128,"environment":129},"authors' Irma3D exploration data","room 1, Scan 1","research building at Jacobs University Bremen (room 1 office)",{"dataset":127,"sequence":131,"environment":129},"room 1, Scan 2",{"dataset":127,"sequence":133,"environment":129},"room 1, Scan 3",[135,137],{"name":136,"methodId":5,"linkable":75,"proposed":75,"self":75},"3D NBV (combined 2D and 3D exploration)",{"name":138,"methodId":5,"linkable":75,"proposed":71,"self":75},"2D NBV only",[140,144,147,150,152,154,156,158,160,161,163,165,167,169,171],[141,141,141,142,143,141,143,143,141],0,4068,-1,[141,145,141,146,143,141,143,143,141],1,2275,[141,148,141,149,143,141,143,143,141],2,1025,[141,141,145,151,143,141,143,143,141],5321,[141,145,145,153,143,141,143,143,141],1524,[141,148,145,155,143,141,143,143,141],361,[141,141,148,157,143,141,143,143,141],6106,[141,145,148,159,143,141,143,143,141],1192,[141,148,148,141,143,141,143,143,141],[145,141,141,162,143,141,143,143,141],4033,[145,145,141,164,143,141,143,143,141],2320,[145,148,141,166,143,141,143,143,141],1017,[145,141,145,168,143,141,143,143,141],4958,[145,145,145,170,143,141,143,143,141],2036,[145,148,145,172,143,141,143,143,141],703,[],[114],[],[],[178],"Two additional exploration experiments in room 1 from the same start position, one with 2D exploration only and one with 2D plus 3D NBV planning",{"slug":180,"group":181,"sourceId":5,"sourceLabel":6,"table":182,"selfRows":183,"metrics":184,"seqs":188,"entrants":192,"cells":195,"outcomes":213,"locators":214,"hardware":215,"wordings":216,"notes":217},"borrmann2014thermalmapping-table-1","borrmann2014thermalmapping:Table 1","Table 1",9,[185,186,187],{"label":118,"unit":119,"statistic":120,"alignment":49},{"label":122,"unit":119,"statistic":120,"alignment":49},{"label":124,"unit":119,"statistic":120,"alignment":49},[189,190,191],{"dataset":127,"sequence":128,"environment":129},{"dataset":127,"sequence":131,"environment":129},{"dataset":127,"sequence":133,"environment":129},[193],{"name":194,"methodId":5,"linkable":75,"proposed":75,"self":75},"combined 2D and 3D NBV exploration",[196,198,200,202,204,206,208,210,212],[141,141,141,197,143,141,143,143,141],4091,[141,141,145,199,143,141,143,143,141],6207,[141,141,148,201,143,141,143,143,141],7034,[141,145,141,203,143,141,143,143,141],2104,[141,145,145,205,143,141,143,143,141],1248,[141,145,148,207,143,141,143,143,141],1207,[141,148,141,209,143,141,143,143,141],965,[141,148,145,211,143,141,143,143,141],203,[141,148,148,141,143,141,143,143,141],[],[182],[],[],[218],"Voxel counts of the 3D model of room 1 during the full exploration run (0.2 m voxels, field-of-view constraint of the thermal camera, stop threshold Vmin = 15 voxels)",{"slug":220,"group":221,"sourceId":5,"sourceLabel":6,"table":222,"selfRows":223,"metrics":224,"seqs":231,"entrants":240,"cells":243,"outcomes":256,"locators":257,"hardware":258,"wordings":259,"notes":260},"borrmann2014thermalmapping-table-3","borrmann2014thermalmapping:Table 3","Table 3",6,[225,228],{"label":226,"unit":227,"statistic":120,"alignment":49},"execution time of the reconstruction algorithm","s",{"label":229,"unit":230,"statistic":120,"alignment":49},"number of polygons","polygons",[232,236,238],{"dataset":233,"sequence":234,"environment":235},"authors' Irma3D data (part of the dataset)","subdivision 50","research building at Jacobs University Bremen (part of the dataset; Sec. 6.5 text says a scan in room 1, the Fig. 12 caption says scan position 12 in room 3)",{"dataset":233,"sequence":237,"environment":235},"subdivision 100",{"dataset":233,"sequence":239,"environment":235},"subdivision 150",[241],{"name":242,"methodId":5,"linkable":75,"proposed":75,"self":75},"probabilistic marching cubes reconstruction",[244,246,248,250,252,254],[141,141,141,245,143,141,143,143,141],6.2,[141,145,141,247,143,141,143,143,141],41322,[141,141,145,249,143,141,143,143,141],24.9,[141,145,145,251,143,141,143,143,141],195824,[141,141,148,253,143,141,143,143,141],55.1,[141,145,148,255,143,141,143,143,141],501229,[],[222],[],[],[261],"Marching-cubes reconstruction of part of the dataset with different spatial subdivisions",{"slug":263,"group":264,"sourceId":5,"sourceLabel":6,"table":265,"selfRows":145,"metrics":266,"seqs":269,"entrants":272,"cells":275,"outcomes":278,"locators":279,"hardware":281,"wordings":282,"notes":283},"borrmann2014thermalmapping-text-sec-5","borrmann2014thermalmapping:Text Sec.5","Text Sec.5",[267],{"label":268,"unit":227,"statistic":120,"alignment":49},"time per scan (3 min 15 s)",[270],{"dataset":127,"sequence":271,"environment":129},"per scanning position",[273],{"name":274,"methodId":5,"linkable":75,"proposed":75,"self":75},"Irma3D stop-and-go scanning",[276],[141,141,141,277,143,141,143,143,141],195,[],[280],"Sec. 5",[],[],[284],"Duration of one 3D scan with thermal and colour image acquisition at a scanning position",[],1790510656003]