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This architecture splits the inference problem into a low-latency update that incorporates new measurements using just the most recent states (filter), and a high-latency update that is capable of closing long loops and smooths using all past states (smoother). This architecture employs the probabilistic graphical models of factor graphs, which allows the low-latency inference and high-latency inference to be viewed as sub-operations of a single optimization performed within a single graphical model. A specific factorization of the full joint density is employed that allows the different inference operations to be performed asynchronously while still recovering the optimal solution produced by a full batch optimization. Due to the real-time, asynchronous nature of this algorithm, updates to the state estimates from the high-latency smoother will naturally be delayed until the smoother calculations have completed. This architecture has been tested within a simulated aerial environment and on real data collected from an autonomous ground vehicle. In all cases, the concurrent architecture is shown to recover the full batch solution, even while updated state estimates are produced in real-time.<\/jats:p>","DOI":"10.1177\/0278364914531056","type":"journal-article","created":{"date-parts":[[2014,7,14]],"date-time":"2014-07-14T23:52:44Z","timestamp":1405381964000},"page":"1544-1568","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":23,"title":["Concurrent filtering and smoothing: A parallel architecture for real-time navigation and full smoothing"],"prefix":"10.1177","volume":"33","author":[{"given":"Stephen","family":"Williams","sequence":"first","affiliation":[{"name":"Institute for Robotics and Intelligent Machines, Georgia Institute of Technology, Atlanta, GA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vadim","family":"Indelman","sequence":"additional","affiliation":[{"name":"Institute for Robotics and Intelligent Machines, Georgia Institute of Technology, Atlanta, GA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael","family":"Kaess","sequence":"additional","affiliation":[{"name":"Field Robotics Center, Robotics Institute, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Richard","family":"Roberts","sequence":"additional","affiliation":[{"name":"Institute for Robotics and Intelligent Machines, Georgia Institute of Technology, Atlanta, GA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"John J.","family":"Leonard","sequence":"additional","affiliation":[{"name":"Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Frank","family":"Dellaert","sequence":"additional","affiliation":[{"name":"Institute for Robotics and Intelligent Machines, Georgia Institute of Technology, Atlanta, GA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2014,7,14]]},"reference":[{"key":"bibr1-0278364914531056","doi-asserted-by":"publisher","DOI":"10.1007\/s00222-008-0157-3"},{"key":"bibr2-0278364914531056","doi-asserted-by":"publisher","DOI":"10.1109\/TAES.2002.1039398"},{"key":"bibr3-0278364914531056","volume-title":"Multitarget-multisensor Tracking: Principles and Techniques","author":"Bar-Shalom Y","year":"1995"},{"key":"bibr4-0278364914531056","doi-asserted-by":"publisher","DOI":"10.1145\/1024074.1024079"},{"key":"bibr5-0278364914531056","doi-asserted-by":"publisher","DOI":"10.1177\/0278364906072768"},{"key":"bibr6-0278364914531056","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2006.886264"},{"key":"bibr7-0278364914531056","volume-title":"Aided Navigation: GPS with High Rate Sensors","author":"Farrell J","year":"2008"},{"key":"bibr8-0278364914531056","doi-asserted-by":"publisher","DOI":"10.1109\/ROBOT.2004.1307180"},{"key":"bibr9-0278364914531056","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"bibr10-0278364914531056","doi-asserted-by":"publisher","DOI":"10.15607\/RSS.2007.III.009"},{"key":"bibr11-0278364914531056","unstructured":"GTSAM (n.d.) 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