[{"data":1,"prerenderedAt":90},["ShallowReactive",2],{"method-lu_milios1997":3},{"method":4,"reference":50,"equipment":69,"figures":89,"results":47},{"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":26,"sensors":32,"platform":35,"estimator":37,"association":38,"timeModel":39,"deskew":40,"loopClosure":41,"globalOptimization":42,"mapRepresentation":43,"prior":44,"outputGeometry":45,"compute":46,"codeUrl":47,"codeLicense":48,"relatedVersions":49},"lu_milios1997","Lu & Milios, 1997","Lu-Milios global scan alignment","Globally Consistent Range Scan Alignment for Environment Mapping",1997,"classic","C01","full_slam_with_global_correction","本文把多幅距離掃描的一致化配準（registration）表述為「位姿網路」上的最佳估計：每幅掃描以機器人位姿為局部座標，掃描對匹配與里程計分別提供強連結與弱連結的相對位姿約束，再以最大概似準則同時求解所有位姿。作者以反覆線性化求解，文中表示約四到五次迭代即收斂。作者明言方法假設機器人停下來取得完整掃描，連續掃描造成的量測時間不一致（即今日的去畸變問題）不在本文範圍內。","Casts consistent multi-scan registration as maximum-likelihood estimation of all scan poses from odometry and scan-matching relations, the forerunner of pose-graph SLAM back-ends.","full_text_reviewed","peer_reviewed_published","main_body","未在營建場域驗證。真實資料包括德國 Ulm FAW 餐廳與走廊的 30 幅掃描（約每 2 m 一幅），以及一段機器人在走廊往返多次的 Pioneer 資料，皆屬既有建築內部，且只以圖示呈現，沒有獨立參考量測（Sec. 5.2, Fig. 8, Fig. 9）。作者指出該走廊缺乏可沿走廊方向定位的特徵，端點的大角度轉彎引入大的旋轉誤差，這與工地長廊或隧道的退化問題相呼應；其「停下掃描」假設也和現代行進中掃描的去畸變需求形成對比（推論）。",[20,21],"simulation","completed_building",[23,24,25],"In the simulated loop, uncorrected odometry pose errors accumulated while globally corrected errors stayed bounded; local scan-to-scan correction also reduced errors significantly but they 'can still potentially grow without bound', and global registration was more accurate than local correction in this example (Sec. 5.2, Fig. 7).","Does not require a priori covariances between object frames, only individual measurement variances (Sec. 2.4; Sec. 6).","Iterative re-linearization typically converges in four or five iterations (Sec. 5.1).",[27,28,29,30,31],"[\"Assumes stop-and-scan acquisition","motion during continuous scanning is outside scope (Sec. 6).\", \"Most expensive step is inverting a 3n x 3n matrix for n poses (Sec. 5.1, Sec. 6).\", \"Developed for 2D","3D generalization only stated as possible (Sec. 6).\", \"Scan-match covariance assumes independent, identically distributed zero-mean Gaussian point errors with a diagonal covariance","the authors say these assumptions are probably difficult to justify but believe they are reasonable in practice (Sec. 4.2).\", \"The sequential state grows with every new pose","the authors propose fixing the relative pose of the most strongly correlated pair or decomposing the network, and state that decomposition can give a sub-optimal estimate when the network is strongly connected (Sec. 4.5).\", \"(inference) Real-data results are shown only visually (Fig. 8, Fig. 9) without an independent reference measurement (Sec. 5.2).\"]",[33,34],"2D laser range finder","wheel odometry",[36,20],"wheeled UGV","maximum-likelihood (weighted least squares) over a pose network, closed-form linear solution iterated with re-linearization","pairwise scan matching (point-to-point matching or an extension of Cox's point-to-line matching) initialised from odometry, producing corresponding point sets; before matching, points likely not visible from the other pose are discarded, and a strong link is created only when the overlapping spatial extent exceeds a fixed fraction of the extent covered by both scans; with the 220 degree sensor, similar headings are also needed for overlap (Sec. 2.1, 2.2, 5.2)","discrete poses (one pose per scan)","none; the approach assumes the robot stops to collect each complete scan, and continuous-scan distortion is declared out of scope (Sec. 6)","implicit: any sufficiently overlapping scan pair creates a strong link, including revisits","batch joint maximum-likelihood estimation of all scan poses with one pose fixed as reference (spring-energy analogy), solved in closed form per linearization and iterated (Sec. 2.3, 3.2, 5.1); sequential variant accumulating G and B per measurement set, with state reduction by fixing the relative pose of the most correlated pair or by network decomposition (Sec. 4.5)","set of registered 2D range scans (point sets) attached to estimated poses","none","globally registered 2D scan points and pose estimates with covariance","offline batch or sequential; no hardware or runtime reported; strong-link terms reduce to simple summations and weak-link terms to 3x3 products, while inverting the 3n x 3n matrix G dominates (Sec. 5.1, Sec. 6)",null,"not_applicable",[],{"id":5,"kind":51,"shortName":7,"title":8,"authors":52,"year":9,"venue":55,"venueType":56,"publisher":57,"volumeIssuePages":58,"doi":59,"arxivId":47,"url":60,"firstPublicDate":61,"publicationStatus":16,"metadataStatus":62,"fulltextStatus":15,"era":10,"classicReason":63,"codeUrl":47,"cluster":11,"topics":64,"mdpi":65,"verification":66,"label":6,"fulltextRoute":67,"versionRead":68,"addedByCensus":65},"method",[53,54],"Feng Lu","Evangelos Milios","Autonomous Robots","journal","Springer (Kluwer Academic Publishers at time of publication)","4(4):333-349","10.1023\u002Fa:1008854305733","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1023\u002FA:1008854305733.pdf","1997-10","metadata_verified","principle reused: formulates multi-scan registration as maximum-likelihood estimation of all scan poses from a network of pairwise pose relations; Cadena et al. (2016, Sec. II) state that the de-facto MAP formulation of modern SLAM back-ends originates in this paper.",[11],false,"corrected","NTU institutional (curl)","version of record, Autonomous Robots 4:333-349 (1997), Kluwer\u002FSpringer PDF, 17 pages",[70,77,81,85],{"category":71,"model":72,"canonical":72,"role":73,"dataset":74,"specs":75,"locator":76},"lidar","Ladar 2D IBEO Lasertechnik","dataset sensor","FAW Ulm cafeteria and corridor scans (30 scans, collected by FAW staff)","2D laser range sensor, maximum viewing angle 220 degrees","Sec. 5.2",{"category":78,"model":79,"canonical":79,"role":73,"dataset":74,"specs":80,"locator":76},"platform","AMOS robot","not_reported",{"category":71,"model":82,"canonical":82,"role":73,"dataset":83,"specs":80,"locator":84},"SICK laser range scanner","Hallway run provided by Steffen Gutmann (Fig. 9)","Sec. 5.2, Fig. 9, note 2",{"category":78,"model":86,"canonical":86,"role":73,"dataset":83,"specs":87,"locator":88},"RWI Pioneer","low-cost platform with odometry error significantly higher than the more expensive platforms used in the other experiments","Sec. 5.2, Fig. 9",[],1790510661746]