[{"data":1,"prerenderedAt":206},["ShallowReactive",2],{"method-gawel2019fabricatorloc":3},{"method":4,"reference":61,"equipment":91,"figures":128,"results":129},{"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":23,"limitations":27,"sensors":33,"platform":38,"estimator":40,"association":41,"timeModel":42,"deskew":43,"loopClosure":44,"globalOptimization":45,"mapRepresentation":46,"prior":47,"outputGeometry":48,"compute":49,"codeUrl":50,"codeLicense":51,"relatedVersions":52},"gawel2019fabricatorloc","Gawel et al., 2019","In situ Fabricator BIM-referenced localization","A Fully-Integrated Sensing and Control System for High-Accuracy Mobile Robotic Building Construction",2019,"recent","C11b","localization_in_prior_map_or_bim","作者為現地建造用的移動機械臂設計整合感測與控制系統。狀態估計以移動視窗估測器融合三件資訊：運動補償後的 VLP-16 掃描對建築模型網格取樣點雲的點到平面 ICP 位姿、IMU 以及輪式里程計，使機器人直接在建築模型座標中定位。接近施工位置時，系統再以末端執行器上三個互相正交的雷射測距儀，在多個臂姿下量測到附近牆面的距離，與模型網格光線追蹤出的平面比對，只最佳化基座位置以取得局部高精度定位。任務由 COMPAS 建築任務介面自動產生，並由全身 MPC 追蹤末端軌跡；最終點位以 Leica Nova TM50 全測站量測評估。","Mobile construction robot that localizes in the 3D building model by ICP of VLP-16 scans against a model-sampled cloud fused with IMU and wheel odometry in a moving horizon estimator, then refines its position near each task with end-effector laser distance sensors matched to model planes; end-effector dot positions checked with a Leica total station.","full_text_reviewed","peer_reviewed_published","main_body","實驗在作者描述為接近真實施工條件的環境中進行（未完成的混凝土牆、少量雜物、模型與現況有偏差），並以 Leica Nova TM50 全測站量測末端執行器打點位置作為參考；文中未說明是否為施工中工地，所以歸為類工地的受控實驗（Sec. IV-B）。結果顯示以建築模型為參考的定位精度，會受到參考牆距離與模型未反映的現況偏差影響（Sec. IV-C）。",[20,21,22],"controlled_experiment","independent_reference","task_level_validation",[24,25,26],"Mean relative errors of 3.3 to 5.9 mm within the 9-dot patterns, supporting locally repeatable tasks (Table I; Sec. IV-C)","Sub-cm absolute positioning (7.0 mm mean) at location A using only on-board sensing (Table I; Abstract)","Reliable operation over 30 experiments including autonomous loops between task locations and re-planning around dynamic obstacles (Sec. IV-C)",[28,29,30,31,32],"Absolute error strongly depends on the task location: 32.9 mm and 26.1 mm mean at locations B and C (Table I)","Distant lateral reference walls (11.5 m at B) magnify small rotational calibration errors of the laser distance sensors (Sec. IV-C2)","Floor inclination at C and brick walls not represented in the building model add error; a simplified model increased relative errors at C by factors of 2 to 3 (Sec. IV-C2)","HAL orientation estimate was less reliable than the LiDAR estimate and is kept fixed; the HAL routine is hand-designed (Sec. III-B, V)","Global localization is out of scope; an approximate initial guess from the known start is needed (Sec. III-A)",[34,35,36,37],"3D LiDAR (Velodyne VLP-16)","IMU (Xsens MTi-100)","wheel encoders","three orthogonal laser distance sensors on the end-effector",[39],"mobile manipulator (Inspector Bots Super Mega Bot base with Kinova Jaco 6-DoF arm, named Waco)","ConFusion moving horizon estimator fusing ICP pose updates against the building model, IMU and wheel odometry, with IMU forward propagation to compensate LiDAR latency; near task locations a separate high-accuracy localization (HAL) optimizes the static base position from laser distance measurements ray-traced against the 3D mesh, keeping orientation fixed (Sec. III-A, III-B)","point-to-plane ICP between motion-compensated LiDAR scans and a point cloud sampled from the 3D CAD triangle mesh; HAL finds the intersected mesh planes by ray tracing with a Cauchy robust cost (Sec. III-A, III-B)","moving-horizon optimization over discrete states; IMU propagation between updates","motion-compensated 3D LiDAR scans (method not detailed) (Sec. III-A)","none (localization in a known building model)","none","point cloud sampled from the 3D CAD triangle mesh of the building model; Octomap occupancy map initialized from the model and updated from LiDAR scans for planning","3D building model (CAD triangle mesh from COMPAS or Rhino Grasshopper task interface); known approximate starting location","robot base and end-effector poses in the building-model frame; no point-cloud map product evaluated","on-board Intel Core i7-6700 CPU @ 3.4 GHz with 16 GB RAM; whole-body MPC at about 100 Hz; off-board computer for the building task interface (Sec. III, IV-A)",null,"not_applicable",[53,57],{"relation":54,"title":55,"doi_or_url":56},"preprint","arXiv 1912.01870 v1","https:\u002F\u002Farxiv.org\u002Fabs\u002F1912.01870",{"relation":58,"title":59,"doi_or_url":60},"repository_copy","ETH Research Collection record","https:\u002F\u002Fdoi.org\u002F10.3929\u002Fethz-b-000386713",{"id":5,"kind":62,"shortName":7,"title":8,"authors":63,"year":9,"venue":76,"venueType":77,"publisher":78,"volumeIssuePages":79,"doi":80,"arxivId":81,"url":82,"firstPublicDate":83,"publicationStatus":16,"metadataStatus":84,"fulltextStatus":15,"era":10,"classicReason":51,"codeUrl":50,"cluster":11,"topics":85,"mdpi":86,"verification":87,"label":6,"fulltextRoute":88,"versionRead":89,"addedByCensus":90},"method",[64,65,66,67,68,69,70,71,72,73,74,75],"Abel Gawel","Hermann Blum","Johannes Pankert","Koen Krämer","Luca Bartolomei","Selen Ercan","Farbod Farshidian","Margarita Chli","Fabio Gramazio","Roland Siegwart","Marco Hutter","Timothy Sandy","2019 IEEE\u002FRSJ International Conference on Intelligent Robots and Systems (IROS)","conference","IEEE","pp. 2300-2307","10.1109\u002Firos40897.2019.8967733","1912.01870","https:\u002F\u002Fdoi.org\u002F10.1109\u002FIROS40897.2019.8967733","2019-11","metadata_verified",[11],false,"corrected","arXiv","arXiv v1 (2019-12-04); IEEE IROS 2019 version of record not compared",true,[92,98,103,107,112,117,122],{"category":93,"model":94,"canonical":94,"role":95,"dataset":50,"specs":96,"locator":97},"lidar","Velodyne VLP-16","method input","3D LiDAR for model-based ICP localization and obstacle updates","Sec. IV-A",{"category":99,"model":100,"canonical":100,"role":95,"dataset":50,"specs":101,"locator":102},"imu","Xsens MTi-100","process constraint in the MHE and latency compensation","Sec. IV-A, Sec. III-A3",{"category":104,"model":36,"canonical":36,"role":95,"dataset":50,"specs":105,"locator":106},"wheel_or_leg_odometry","differential drive kinematic model; low confidence while wheels turn","Sec. III-A2, Sec. IV-A",{"category":108,"model":109,"canonical":109,"role":95,"dataset":50,"specs":110,"locator":111},"other","three orthogonal laser distance measurement sensors (sensor head at end-effector)","used for high-accuracy localization against model planes, six end-effector poses in the experiment","Sec. III-B, Sec. IV-A, Fig. 3",{"category":113,"model":114,"canonical":114,"role":95,"dataset":50,"specs":115,"locator":116},"platform","Inspector Bots Super Mega Bot mobile base with Kinova Jaco arm (robot 'Waco')","skid-steered four-wheel base, 6-DoF arm, custom 3D-printed end-effector with spring-loaded marker","Sec. IV-A, Fig. 3",{"category":118,"model":119,"canonical":119,"role":120,"dataset":50,"specs":121,"locator":97},"compute","Intel Core i7-6700 CPU @ 3.4 GHz, 16 GB RAM","compute for runtime","on-board computer; separate off-board computer over WLAN for the building task interface",{"category":123,"model":124,"canonical":124,"role":125,"dataset":50,"specs":126,"locator":127},"total_station","Leica Nova TM50","reference or ground truth","measures commanded task locations and final end-effector placements","Sec. IV-B",[],{"totalRows":130,"groupCount":131,"groups":132,"others":205},7,2,[133,180],{"slug":134,"group":135,"sourceId":5,"sourceLabel":6,"table":136,"selfRows":137,"metrics":138,"seqs":146,"entrants":155,"cells":158,"outcomes":173,"locators":174,"hardware":176,"wordings":177,"notes":178},"gawel2019fabricatorloc-table-i","gawel2019fabricatorloc:Table I","Table I",6,[139,144],{"label":140,"unit":141,"statistic":142,"alignment":143},"Mean absolute error (end-effector dot position, global coordinates w.r.t. building model origin)","mm","mean","not_reported",{"label":145,"unit":141,"statistic":142,"alignment":51},"Mean relative error (pair-wise within the 9-dot pattern)",[147,151,153],{"dataset":148,"sequence":149,"environment":150},"authors' construction-like test site","task location A","realistic construction-like environment with unfinished concrete walls and some clutter",{"dataset":148,"sequence":152,"environment":150},"task location B",{"dataset":148,"sequence":154,"environment":150},"task location C",[156],{"name":157,"methodId":5,"linkable":90,"proposed":90,"self":90},"proposed system (LiDAR-to-model ICP + MHE + HAL + whole-body MPC)",[159,162,165,167,169,171],[160,160,160,130,161,160,161,161,160],0,-1,[160,163,160,164,161,160,161,161,160],1,3.3,[160,160,163,166,161,160,161,161,160],32.9,[160,163,163,168,161,160,161,161,160],3.8,[160,160,131,170,161,160,161,161,160],26.1,[160,163,131,172,161,160,161,161,160],5.9,[],[175],"Table I; Sec. IV-C",[],[],[179],"Autonomous approach from random locations several metres away, HAL with three laser distance sensors, then marking a 50 mm spaced 3 x 3 dot pattern on a wall; commanded and executed dot positions measured with a Leica Nova TM50 total station; distance from task location to the lateral HAL reference wall 1.7 m (A), 11.5 m (B), 4 m (C)",{"slug":181,"group":182,"sourceId":5,"sourceLabel":6,"table":183,"selfRows":163,"metrics":184,"seqs":188,"entrants":190,"cells":193,"outcomes":196,"locators":198,"hardware":200,"wordings":202,"notes":203},"gawel2019fabricatorloc-text-sec-iii","gawel2019fabricatorloc:Text Sec.III","Text Sec.III",[185],{"label":186,"unit":187,"statistic":143,"alignment":51},"MPC strategy running at approximately 100 Hz","Hz",[189],{"dataset":51,"sequence":51,"environment":51},[191],{"name":192,"methodId":5,"linkable":90,"proposed":90,"self":90},"whole-body MPC (OCS2)",[194],[160,160,160,195,160,160,160,161,160],100,[197],"other: approximate ('~100 Hz')",[199],"Sec. III",[201],"not_reported (the paper lists an on-board Intel Core i7-6700 CPU @ 3.4 GHz with 16 GB RAM but does not state which computer runs the MPC)",[],[204],"Rate of the whole-body MPC that generates base and end-effector reference trajectories",[],1790510657147]