[{"data":1,"prerenderedAt":85},["ShallowReactive",2],{"method-olson2010passivesync":3},{"method":4,"reference":46,"equipment":64,"figures":84,"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":31,"platform":34,"estimator":36,"association":37,"timeModel":38,"deskew":37,"loopClosure":37,"globalOptimization":39,"mapRepresentation":37,"prior":40,"outputGeometry":41,"compute":42,"codeUrl":43,"codeLicense":44,"relatedVersions":45},"olson2010passivesync","Olson, 2010","Passive synchronization","A passive solution to the sensor synchronization problem",2010,"classic","C13","sensing_calibration_sync_preprocessing","許多商用感測器不支援同步，只能在資料抵達主機時打時間戳記，而緩衝與非即時作業系統造成的抖動在高負載時可達數百毫秒。本文利用延遲不可能為負的因果關係，搭配感測器時鐘速率漂移的上界模型，以取最大值的規則估計感測器與主機時鐘的偏移，再還原每筆資料的主機時間。演算法可單向即時執行或前後兩次處理，作者證明結果不會劣於直接以抵達時間打戳記，但實驗僅為合成資料。","Passive algorithm reducing timestamp error for sensors lacking synchronization support by modelling host-induced jitter.","full_text_reviewed","peer_reviewed_published","supplementary","未在工地或測繪載具上評估。文中舉例：以每秒 90 度旋轉、觀測 10 m 外物體時，10 ms 的同步誤差即造成 15.7 cm 投影誤差；經 USB 轉序列埠連接的 LiDAR 與 IMU 也會引入可變延遲。這對以低成本感測器自組的工地掃描設備具參考價值（推論）。",[20],"simulation",[22,23,24],"Provably no worse than naive arrival-time stamping; no real-time OS needed (abstract)","Never places an observation earlier than it occurred and never does worse than naive stamping (Claims 1 and 2, Sec. III-C)","In synthetic tests with uniform latency up to 0.5 s at 1 s intervals, naive stamping averaged 0.25 s error while both variants were substantially lower, the bidirectional one best (Fig. 6 caption, Sec. IV)",[26,27,28,29,30],"Validated only on synthetic data (reviewer observation, Sec. IV)","Performance approaches the no-synchronization case as sensor clock drift grows (Fig. 6 caption, Sec. IV)","Offset error increases as the time between observations grows (Fig. 7, Sec. IV)","The causal online variant is less accurate than the non-causal bidirectional variant (Sec. III-D, IV)","Needs sensor timestamps or a regular message rate acting as a clock, and drift bounds must be specified (Sec. III)",[32,33],"generic sensors without synchronization support (examples in text: SICK and Hokuyo LIDARs and Xsens IMUs behind USB-to-serial converters)","deployment context only: 12 SICK LIDARs, a Velodyne HDL-64E, 15 Delphi ACC radars and an Applanix IMU\u002FGPS on MIT's DARPA Urban Challenge vehicle (not evaluated in this paper)",[35],"simulation (synthetic timing data only)","max-rule lower-bound estimate of the sensor-to-host clock offset using causality (latency is non-negative) and a bounded clock-rate drift model with parameters alpha1 and alpha2; two-pass O(N) algorithm (causal forward pass plus optional backward pass)","not_applicable","per-message recovery of host time t = p - A(p), with a time-varying clock offset A(p) bounded by the rate-drift model","none","user-specified bounds on the sensor-to-host clock-rate error (alpha1, alpha2) that define the drift model; optionally a known minimum system latency; sensor timestamps or a regular message rate acting as a clock","corrected timestamps","O(1) per observation in causal online mode and O(N) for the bidirectional two-pass version; a few lines of code; no real-time OS required",null,"not_verified",[],{"id":5,"kind":47,"shortName":7,"title":8,"authors":48,"year":9,"venue":50,"venueType":51,"publisher":52,"volumeIssuePages":53,"doi":54,"arxivId":43,"url":55,"firstPublicDate":56,"publicationStatus":16,"metadataStatus":57,"fulltextStatus":15,"era":10,"classicReason":58,"codeUrl":43,"cluster":11,"topics":59,"mdpi":60,"verification":61,"label":6,"fulltextRoute":62,"versionRead":63,"addedByCensus":60},"method",[49],"E. Olson","2010 IEEE\u002FRSJ International Conference on Intelligent Robots and Systems","conference","IEEE","pp. 1059-1064","10.1109\u002Firos.2010.5650579","https:\u002F\u002Fdoi.org\u002F10.1109\u002FIROS.2010.5650579","2010-10","metadata_verified","necessary technical node: host-side timestamp correction for sensors without synchronization support, a common situation for low-cost mapping rigs.",[11],false,"confirmed","NTU institutional (curl)","Version of record, IEEE\u002FRSJ IROS 2010 pp. 1059-1064 (IEEE Xplore PDF)",[65,71,74,77,80],{"category":66,"model":67,"canonical":67,"role":68,"dataset":43,"specs":69,"locator":70},"lidar","Velodyne HDL-64E","method input","one unit on MIT's DARPA Urban Challenge vehicle, synchronized with this algorithm; deployment context, not evaluated in the paper","Sec. I",{"category":66,"model":72,"canonical":72,"role":68,"dataset":43,"specs":73,"locator":70},"SICK LIDAR (12 units; model not stated)","deployment context on the DARPA Urban Challenge vehicle; not evaluated",{"category":75,"model":76,"canonical":76,"role":68,"dataset":43,"specs":73,"locator":70},"radar","Delphi ACC Radar (15 units)",{"category":78,"model":79,"canonical":79,"role":68,"dataset":43,"specs":73,"locator":70},"gnss","Applanix IMU\u002FGPS",{"category":81,"model":82,"canonical":82,"role":68,"dataset":43,"specs":83,"locator":70},"platform","MIT DARPA Urban Challenge vehicle","vehicle on which the algorithm was originally developed and used",[],1790510664269]