[{"data":1,"prerenderedAt":79},["ShallowReactive",2],{"method-smith_self_cheeseman1990":3},{"method":4,"reference":57,"equipment":77,"figures":78,"results":41},{"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":24,"sensors":28,"platform":29,"estimator":31,"association":32,"timeModel":33,"deskew":34,"loopClosure":35,"globalOptimization":36,"mapRepresentation":37,"prior":38,"outputGeometry":39,"compute":40,"codeUrl":41,"codeLicense":34,"relatedVersions":42},"smith_self_cheeseman1990","Smith et al., 1990","Stochastic map","Estimating Uncertain Spatial Relationships in Robotics",1990,"classic","C01","estimation_framework_or_library","本文提出「隨機地圖（stochastic map）」：把機器人與各物件之間的空間關係組成一個狀態向量，同時保存其平均值與完整共變異數矩陣，以描述關係之間的相依性。作者以一階線性化推導位姿複合（compounding）與反轉運算的共變異數傳遞，並以（擴展）卡爾曼濾波器（Kalman filter）在新量測加入時遞迴更新整張地圖。文中明示兩項前提：角度誤差須夠小以支持線性化，且只估計前兩階動差即足以支援決策（Sec. 6）。","Represents all uncertain spatial relations in one state vector with full covariance, propagates uncertainty through first-order compounding, and updates the map with an (extended) Kalman filter.","full_text_reviewed","peer_reviewed_published","main_body","未在營建場域測試。其「共變異數傳遞與關係相依性」觀念是之後討論 SLAM 點雲座標不確定性與誤差累積的理論起點（推論）。",[20],"simulation",[22,23],"Probabilistic estimates are presented as less conservative than earlier worst-case min-max bounds (abstract; Sec. 1).","Allows expressing the uncertainty of any frame relative to any other frame and predicting in advance whether accumulated uncertainty will make an operation fail (Sec. 1; Sec. 6).",[25,26,27],"[\"Relies on small angular errors because inherently nonlinear relations are linearized (Sec. 6).\", \"Assumes the first two moments are adequate for decision making (Sec. 6).\", \"The EKF is a sub-optimal nonlinear estimator","the iterated EKF is offered to reduce the error due to nonlinearities in the measurement function (Sec. 4.2.3).\", \"The 6-DoF Jacobians contain singular angle combinations near which covariance accuracy decreases","avoidance methods were still being explored (Appendix A).\", \"Full-covariance filtering implies computation growing with the square of the number of landmarks, as later noted by Durrant-Whyte and Bailey (2006, Part I Sec. II) and Dissanayake et al. (2001, Sec. V).\"]",[],[30],"[\"simulation\"]","extended Kalman filter on a joint state of robot and object frames (iterated EKF also given)","no dedicated data-association algorithm; the running example uses the stochastic map to decide that a newly sensed object cannot be the previously mapped object #1 (Sec. 2.3), Sec. 6 mentions ignoring sensor results that are too improbable, and the developed example assumes the sensor identifies the re-observed object as object #1, noting that in practice the new object would first be compared with the old ones (Sec. 5, Step 4)","discrete poses (discrete motion approximation, Sec. 4)","not_applicable","implicit: re-sensing a previously mapped object is incorporated as a constraint that reduces the uncertainty of the robot and all correlated objects (Sec. 2.3 example); no separate loop-closure module","none (recursive filtering)","stochastic map: vector of object\u002Frobot frame relations with full covariance matrix","world frame fixed at the initial robot pose (Sec. 5); prior knowledge can enter as new objects with given world locations (Case I-a, Sec. 4.2.1) or as geometric constraints such as colinearity, coplanarity or a possibly 'noisy' rectangle constraint that is modelled like a sensor measurement (Sec. 4.2, 4.2.2, Sec. 5, Figure 6)","mean and covariance of relative spatial relationships between frames (2D in text; 6-DoF Jacobians in Appendix A)","no runtime or hardware reported; a robot motion changes only the robot entry and its row and column of the covariance matrix (Sec. 4.1); precomputed composite Jacobians are more efficient than the recursive method (Sec. 3.2.3)",null,[43,47,50,54],{"relation":44,"title":45,"doi_or_url":46},"conference_version","Estimating uncertain spatial relationships in robotics (Proc. 1987 IEEE ICRA, p. 850, one-page record)","10.1109\u002Frobot.1987.1087846",{"relation":44,"title":48,"doi_or_url":49},"Estimating Uncertain Spatial Relationships in Robotics (Uncertainty in Artificial Intelligence 2, Machine Intelligence and Pattern Recognition, Elsevier 1988, pp. 435-461)","10.1016\u002Fb978-0-444-70396-5.50042-x",{"relation":51,"title":52,"doi_or_url":53},"preprint","Authors' typeset full-length version posted on arXiv (v2 comment: as published in UAI 2 (1988) and reprinted in Autonomous Robot Vehicles (1990))","https:\u002F\u002Farxiv.org\u002Fabs\u002F1304.3111",{"relation":44,"title":55,"doi_or_url":56},"Estimating Uncertain Spatial Relationships in Robotics (shorter version in Proc. 2nd Conference on Uncertainty in Artificial Intelligence, UAI 1986, pp. 267-288; scanned as arXiv 1304.3111 v1)","https:\u002F\u002Farxiv.org\u002Fabs\u002F1304.3111v1",{"id":5,"kind":58,"shortName":7,"title":8,"authors":59,"year":9,"venue":63,"venueType":64,"publisher":65,"volumeIssuePages":66,"doi":67,"arxivId":68,"url":53,"firstPublicDate":69,"publicationStatus":16,"metadataStatus":70,"fulltextStatus":15,"era":10,"classicReason":71,"codeUrl":41,"cluster":11,"topics":72,"mdpi":73,"verification":74,"label":6,"fulltextRoute":75,"versionRead":76,"addedByCensus":73},"method",[60,61,62],"Randall Smith","Matthew Self","Peter Cheeseman","Autonomous Robot Vehicles (book, Springer New York)","book_chapter","Springer","pp. 167-193","10.1007\u002F978-1-4613-8997-2_14","1304.3111","1986","metadata_verified","principle reused \u002F uncertainty method: defines the stochastic map (joint mean and full covariance of all spatial relations), compounding with first-order covariance propagation, and Kalman-filter updating, which Durrant-Whyte and Bailey (2006, Part I Sec. II) identify as the landmark paper for correlated landmark estimation.",[11],false,"corrected","arXiv","arXiv 1304.3111 v2 (2026-09-14), authors' typeset full-length version stated as published in UAI 2 (1988) and reprinted in Autonomous Robot Vehicles (1990); the Springer 1990 chapter PDF was not accessible (Springer returned only an HTML preview), so page numbers of the 1990 chapter were not checked",[],[],1790510664435]