[{"data":1,"prerenderedAt":188},["ShallowReactive",2],{"method-lips2018":3},{"method":4,"reference":63,"equipment":85,"figures":105,"results":106},{"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":22,"limitations":27,"sensors":33,"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},"lips2018","Geneva et al., 2018","LIPS","LIPS: LiDAR-Inertial 3D Plane SLAM",2018,"recent","C05","full_slam_with_global_correction","LIPS 以「最近點」（closest point, CP）表示平面：取平面上距參考座標原點最近的三維點，作為最小且可加法更新的平面參數。為避免平面通過原點時的奇異性，每個平面以首次觀測的位姿為錨點表示，並推導錨定平面因子；每個影格點雲先以 RANSAC 找出平面點集，再壓縮成帶共變異數的局部 CP 量測。這些平面因子與連續 IMU 預積分因子一起放入因子圖，以 iSAM2 平滑估計，平面重複觀測即形成迴圈約束。","Graph-based LiDAR-inertial plane SLAM that parameterizes infinite planes by their closest point to the frame origin, anchors each plane in its first observing frame to avoid the zero-distance singularity, compresses RANSAC-segmented points into local closest-point measurements with covariance, and fuses them with continuous IMU preintegration in iSAM2.","full_text_reviewed","peer_reviewed_published","supplementary","論文以室內人造環境為目標，模擬為 2D 平面圖垂直拉伸的曼哈頓式建物，真實測試僅在人工擺放平板的小場景中進行，未在營建工地或完成建物中以獨立參考驗證。平面地標的最小參數化對牆、樓板等大平面主導的室內施工場景有參考價值，也與平面型 BIM 元件的對應概念相近，但平面擷取與對應在真實雜亂工地的可行性未經檢驗（推論）。",[20,21],"simulation","controlled_experiment",[23,24,25,26],"Closest-point planes gave lower average RMSE than the relative quaternion plane factor of Kaess in 80 Monte-Carlo runs at 1 cm and 3 cm LiDAR noise (Table II)","Authors observed faster convergence than the quaternion form in full batch optimization (Sec. VI-C)","Re-observing previously seen hallway planes after 300 s rapidly reduced the estimation error, acting as loop closure (Sec. VI-B, Fig. 4)","Real test: 1.5 cm start-to-end difference after a 30 m trajectory, 0.05% of path length (Sec. VI-D, Fig. 7)",[28,29,30,31,32],"Real-world validation is a small proof of concept with planar boards placed around the sensor to ease RANSAC extraction and avoid degenerate motion (Sec. VI-D)","RANSAC plane extraction ran offline and needs replacement or acceleration for real-world use (Sec. VI-D)","Poses may be under-constrained when extracted planes do not constrain all degrees of freedom (Sec. VII)","Simple Mahalanobis-distance plane matching was only shown sufficient for small-scale experiments (Sec. VI-D)","LiDAR-IMU extrinsic estimated manually; no scan deskewing used (Sec. VI-D, III-B)",[34,35],"3D LiDAR (8-beam Quanergy M8 in the real test; simulator modelled on it)","IMU (Microstrain 3DM-GX3-25 in the real test; ADIS16448 model in simulation)",[20,37],"sensor unit (Quanergy M8 with IMU attached underneath) moved in front of planar boards in a small indoor scene; carrier not described (Sec. VI-D, Fig. 6)","graph-based MLE (nonlinear least squares) with continuous IMU preintegration factors and anchored closest-point plane factors, solved incrementally with iSAM2 in GTSAM; Huber loss on plane factors (Sec. III, V-C)","planes extracted from each point cloud with RANSAC plane segmentation (PCL) run offline in the real test; each planar subset compressed to a local closest-point plane with covariance by weighted Gauss-Newton; plane correspondences by a Mahalanobis-distance test (known correspondences in simulation) (Sec. V-C, VI-B, VI-D)","discrete IMU states at LiDAR times linked by closed-form continuous preintegration (Sec. IV)","none in the reported experiments; the authors note preintegration could unwarp clouds at high speed but did not use it (Sec. III-B)","implicit through re-observation of previously estimated plane landmarks (no separate place recognition) (Sec. VI-B)","full smoothing of the IMU state history and plane landmarks with iSAM2 (Sec. III-B)","sparse landmark map of infinite planes, each stored as a closest-point vector anchored in the frame of its first observation (Sec. V-B)","LiDAR-IMU extrinsic estimated manually for the real test (Sec. VI-D); simulator uses the known extrinsic of Table I; no map prior","IMU trajectory and a set of plane parameters; no dense point cloud output is described","measurement compression and iSAM2 estimator ran in real time in the reported tests, but RANSAC plane extraction was run offline and the authors state it must be replaced or accelerated for real use; hardware not reported (Sec. VI-B, VI-D)",null,"not_applicable (only the simulator is released, MIT License)",[51,55,59],{"relation":52,"title":53,"doi_or_url":54},"accepted_manuscript","Author copy on first author's website (IEEE pagination pp. 123-130)","https:\u002F\u002Fpgeneva.com\u002Fdownloads\u002Fpapers\u002FGeneva2018IROS.pdf",{"relation":56,"title":57,"doi_or_url":58},"technical_report_supplement","Technical report RPNG-2018-LIPS with Jacobians (last updated July 31, 2018)","https:\u002F\u002Fudel.edu\u002F~ghuang\u002Fpapers\u002Ftr_lips.pdf",{"relation":60,"title":61,"doi_or_url":62},"dataset","rpng\u002Flips LiDAR-inertial 3D plane simulator (MIT License); no estimator code","https:\u002F\u002Fgithub.com\u002Frpng\u002Flips",{"id":5,"kind":64,"shortName":7,"title":8,"authors":65,"year":9,"venue":70,"venueType":71,"publisher":72,"volumeIssuePages":73,"doi":74,"arxivId":48,"url":75,"firstPublicDate":76,"publicationStatus":16,"metadataStatus":77,"fulltextStatus":15,"era":10,"classicReason":78,"codeUrl":48,"cluster":11,"topics":79,"mdpi":80,"verification":81,"label":6,"fulltextRoute":82,"versionRead":83,"addedByCensus":84},"method",[66,67,68,69],"Patrick Geneva","Kevin Eckenhoff","Yulin Yang","Guoquan Huang","2018 IEEE\u002FRSJ International Conference on Intelligent Robots and Systems (IROS)","conference","IEEE","pp. 123-130","10.1109\u002Firos.2018.8594463","https:\u002F\u002Fdoi.org\u002F10.1109\u002FIROS.2018.8594463","2018-10-01","metadata_verified","not_applicable",[11],false,"corrected","author copy","Author copy of the IROS 2018 paper with IEEE pagination pp. 123-130 (PDF created 2018-11-18), equivalent in layout to the version of record; IEEE Xplore copy not opened",true,[86,92,96,102],{"category":87,"model":88,"canonical":88,"role":89,"dataset":48,"specs":90,"locator":91},"lidar","Quanergy M8","method input","eight-channel LiDAR operating at 10 Hz","Sec. VI-D",{"category":93,"model":94,"canonical":94,"role":89,"dataset":48,"specs":95,"locator":91},"imu","Microstrain 3DM-GX3-25","attached to the bottom of the LiDAR, 500 Hz",{"category":87,"model":97,"canonical":97,"role":98,"dataset":99,"specs":100,"locator":101},"Quanergy M8 (simulated)","dataset sensor","LIPS simulator (extruded floor plan)","simulator modelled on it: 0.25 deg angular resolution, 8 zenith angles from 3.2 to -18.3 deg, 1 cm and 3 cm point deviation, 5 Hz","Sec. VI-A; Table I",{"category":93,"model":103,"canonical":103,"role":98,"dataset":99,"specs":104,"locator":101},"ADIS16448 (simulated)","gyro noise density 0.005 rad\u002Fs\u002Fsqrt(Hz), accel noise density 0.01 m\u002Fs2\u002Fsqrt(Hz), 800 Hz",[],{"totalRows":107,"groupCount":108,"groups":109,"others":187},5,2,[110,161],{"slug":111,"group":112,"sourceId":5,"sourceLabel":6,"table":113,"selfRows":114,"metrics":115,"seqs":124,"entrants":130,"cells":135,"outcomes":155,"locators":156,"hardware":157,"wordings":158,"notes":159},"lips2018-table-ii","lips2018:Table II","Table II",4,[116,121],{"label":117,"unit":118,"statistic":119,"alignment":120},"Average RMSE (position)","m","RMSE","not_reported",{"label":122,"unit":123,"statistic":119,"alignment":120},"Average RMSE (orientation)","deg",[125,128],{"dataset":99,"sequence":126,"environment":127},"LiDAR noise 1 cm","simulated Manhattan-world indoor building (rooms and hallway)",{"dataset":99,"sequence":129,"environment":127},"LiDAR noise 3 cm",[131,133],{"name":132,"methodId":5,"linkable":84,"proposed":84,"self":84},"Closest Point",{"name":134,"methodId":48,"linkable":80,"proposed":80,"self":80},"Quaternion [6] (Kaess relative quaternion factor)",[136,140,143,145,147,149,151,153],[137,137,137,138,139,137,139,139,137],0,0.005,-1,[141,137,137,142,139,137,139,139,137],1,0.016,[137,141,137,144,139,137,139,139,137],0.027,[141,141,137,146,139,137,139,139,137],0.081,[137,137,141,148,139,137,139,139,137],0.012,[141,137,141,150,139,137,139,139,137],0.033,[137,141,141,152,139,137,139,139,137],0.057,[141,141,141,154,139,137,139,139,137],0.126,[],[113],[],[],[160],"Average RMSE over 80 Monte-Carlo simulations on the 180 m simulated indoor trajectory; known plane correspondences; iSAM2; closest-point (CP) versus relative quaternion plane factor",{"slug":162,"group":163,"sourceId":5,"sourceLabel":6,"table":164,"selfRows":141,"metrics":165,"seqs":170,"entrants":175,"cells":177,"outcomes":180,"locators":181,"hardware":183,"wordings":184,"notes":185},"lips2018-text-sec-vi-d","lips2018:Text Sec.VI-D","Text Sec.VI-D",[166],{"label":167,"unit":168,"statistic":120,"alignment":169},"difference between the start and end poses","cm","none",[171],{"dataset":172,"sequence":173,"environment":174},"authors' real-world test (planar boards)","30 m loop","small indoor scene with planar objects placed around the sensor",[176],{"name":7,"methodId":5,"linkable":84,"proposed":84,"self":84},[178],[137,137,137,179,139,137,139,139,137],1.5,[],[182],"Sec. VI-D; Fig. 7",[],[],[186],"Real sensor unit moved in front of planar boards and returned to the start; difference between start and end poses after a 30 m path",[],1790510664178]