[{"data":1,"prerenderedAt":91},["ShallowReactive",2],{"method-soudarissanane2011scanninggeometry":3},{"method":4,"reference":46,"equipment":67,"figures":90,"results":43},{"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":25,"sensors":32,"platform":35,"estimator":37,"association":38,"timeModel":38,"deskew":38,"loopClosure":38,"globalOptimization":39,"mapRepresentation":38,"prior":40,"outputGeometry":41,"compute":42,"codeUrl":43,"codeLicense":44,"relatedVersions":45},"soudarissanane2011scanninggeometry","Soudarissanane et al., 2011","TLS scanning geometry","Scanning geometry: Influencing factor on the quality of terrestrial laser scanning points",2011,"classic","C13","sensing_calibration_sync_preprocessing","作者由簡化的雷達距離方程式推導，指出地面雷射掃描的訊噪比隨入射角餘弦與距離平方下降，並提出入射角係數 cos α 與距離係數；以總體最小平方擬合平面後，把沿雷射束方向的殘差換算為垂直於平面的殘差，藉此分離掃描幾何對單點雜訊的貢獻。實驗先以 Leica HDS6000 掃描 1 m 見方的白色合板（固定 20 m 並旋轉 0° 至 80°，再於 5 m 至 50 m 重複），再以 FARO LS880 HE 從房間中央與角落兩站掃描空房間。房間平均標準差由 3.23 mm 降為去除入射角效應後的 2.55 mm，約 20% 的雜訊來自非零入射角；作者據此建議先以 CAD 圖與初步低解析度掃描評估各測站的入射角與距離，再規劃測站位置。","Models how incidence angle (cos α) and range (squared) degrade TLS signal-to-noise and per-point range noise on planar surfaces, isolates the scan-geometry share with total-least-squares plane fits, and validates it on a rotated reference board (Leica HDS6000, 5 to 50 m, 0° to 80°) and a room scanned from two viewpoints (FARO LS880 HE), where about 20% of the noise stems from non-zero incidence angles (3.23 mm before versus 2.55 mm after correction).","full_text_reviewed","peer_reviewed_published","background","作者以 as-built 模型與結構監測作為誤差傳播的應用情境（Sec. 1），並建議先以 CAD 圖與初步低解析度掃描計算入射角與距離，再決定掃描站位以達到指定精度門檻（Sec. 6）。實驗僅在近實驗室條件的板件與空房間進行，未在施工工地驗證。對 SLAM 點雲而言，這提供以入射角與距離作為點位加權或篩選依據的理論基礎（推論）。",[20],"controlled_experiment",[22,23,24],"Noise growth with incidence angle and range is modelled from the point cloud alone, without external measurements (Sec. 6)","In the room test about 20% of the measurement noise came from non-zero incidence angles; the average standard deviation fell from 3.23 mm to 2.55 mm after removing the incidence-angle effect (Sec. 5.2.3)","Supports planning scanner positions by evaluating incidence angles and ranges from a CAD drawing on a first low-resolution scan, for example to meet a precision standard such as 5 mm (Sec. 6)",[26,27,28,29,30,31],"Only noise levels are modelled; biases are not studied and angular and range errors are assumed uncorrelated (Sec. 3.2)","The range model depends on the scanner and only the Leica HDS6000 and FARO LS880 HE were tested (Sec. 6)","The reference board is not perfectly Lambertian; repetition with Spectralon targets is recommended (Sec. 4.1, 6)","Planar surfaces are assumed, yet the room walls and floor were not perfectly planar (Sec. 3, 5.2.2)","Room ranges of 0 to 6.5 m were too short to test the range effect, and near-perpendicular returns may saturate the detector (Sec. 5.2, 5.2.2)","9 of 54 board scans at long range and high incidence were unusable (standard deviation above 5 mm or fewer than 100 points) (Sec. 4.2)",[33,34],"terrestrial laser scanner Leica HDS6000 (reference board experiments)","terrestrial laser scanner FARO LS880 HE (room experiment, 1\u002F4 of full resolution)",[36],"static terrestrial scanner stations (scanner mounting not described; only the reference board is stated to be on a tripod)","total least squares plane fitting per segment or per 5°x5° spherical patch; beam-direction residuals converted to orthogonal residuals with the incidence-angle coefficient c_I(α) = cos α and a range coefficient c_R(ρ) derived from a simplified radar range equation under a Lambertian assumption","not_applicable","none","local surface normals from plane fits (or a CAD model) to compute per-point incidence angles; scanner-specific range limits (0 m and 80 m used for the HDS6000)","per-point noise levels (beam direction and orthogonal) and per-patch standard deviation maps shown as net-views; no new point cloud product","offline Matlab on a Dell Precision 390 (dual-core Intel CPU 2.13 GHz, 3 GB RAM, NVIDIA Quadro FX 3500, Windows 7); runtime not reported",null,"not_verified",[],{"id":5,"kind":47,"shortName":7,"title":8,"authors":48,"year":9,"venue":53,"venueType":54,"publisher":55,"volumeIssuePages":56,"doi":57,"arxivId":43,"url":58,"firstPublicDate":59,"publicationStatus":16,"metadataStatus":60,"fulltextStatus":15,"era":10,"classicReason":61,"codeUrl":43,"cluster":11,"topics":62,"mdpi":63,"verification":64,"label":6,"fulltextRoute":65,"versionRead":66,"addedByCensus":63},"component",[49,50,51,52],"Sylvie Soudarissanane","Roderik Lindenbergh","Massimo Menenti","Peter Teunissen","ISPRS Journal of Photogrammetry and Remote Sensing","journal","Elsevier","66(4), pp. 389-399","10.1016\u002Fj.isprsjprs.2011.01.005","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.isprsjprs.2011.01.005","2011-02-24","metadata_verified","evaluation-calibration-uncertainty method: canonical geomatics reference on scan geometry (range and incidence angle) as an influence factor on laser point quality.",[11],false,"confirmed","NTU institutional (Chrome)","Version of record, ISPRS Journal of Photogrammetry and Remote Sensing 66(4):389-399 (2011), ScienceDirect HTML full text",[68,74,78,84],{"category":69,"model":70,"canonical":70,"role":71,"dataset":43,"specs":72,"locator":73},"tls_scanner","Leica HDS6000","method input","range-model limits d_min 0 m and d_max 80 m used (Sec. 4.2.2); experiments under near-laboratory conditions","Sec. 4, 4.2.2",{"category":69,"model":75,"canonical":75,"role":71,"dataset":43,"specs":76,"locator":77},"FARO LS880 HE","resolution set to 1\u002F4 of full; room point cloud over 20 million points; written 'LS880 HE80' in Sec. 6","Sec. 5.1, 6",{"category":79,"model":80,"canonical":80,"role":81,"dataset":43,"specs":82,"locator":83},"other","1 x 1 m white coated plywood board on tripod with goniometer","reference or ground truth","rotatable horizontally with 2° precision; considered almost Lambertian","Sec. 4, Fig. 4",{"category":85,"model":86,"canonical":86,"role":87,"dataset":43,"specs":88,"locator":89},"compute","Dell Precision 390","compute for runtime","dual-core Intel CPU 2.13 GHz, 3 GB RAM, NVIDIA Quadro FX 3500, Windows 7, Matlab","Sec. 5.2.3",[],1790510663828]