Show simple item record

dc.contributor.authorWilliams, Stephen
dc.contributor.authorIndelman, Vadim
dc.contributor.authorKaess, Michael
dc.contributor.authorRoberts, Richard
dc.contributor.authorLeonard, John J.
dc.contributor.authorDellaert, Frank
dc.date.accessioned2015-07-14T17:55:36Z
dc.date.available2015-07-14T17:55:36Z
dc.date.issued2014
dc.identifier.citationWilliams, S.; Indelman, V.; Kaess, M.; Roberts, R.; Leonard, J.; & Dellaert, F. (2014). “Concurrent Filtering and Smoothing: A Parallel Architecture for Real-Time Navigation and Full Smoothing,” International Journal of Robotics Research, Vol. 33, (October 2014), pp. 1544-1568.en_US
dc.identifier.urihttp://hdl.handle.net/1853/53685
dc.description© The Author(s) 2014en_US
dc.descriptionDOI: 10.1177/0278364914531056
dc.description.abstractWe present a parallelized navigation architecture that is capable of running in real-time and incorporating long-term loop closure constraints while producing the optimal Bayesian solution. 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.en_US
dc.language.isoen_USen_US
dc.publisherGeorgia Institute of Technologyen_US
dc.subjectFilteringen_US
dc.subjectInformation fusionen_US
dc.subjectProbabilistic graphical modelen_US
dc.subjectReal time navigationen_US
dc.subjectSLAMen_US
dc.subjectSmoothingen_US
dc.titleConcurrent Filtering and Smoothing: A Parallel Architecture for Real-Time Navigation and Full Smoothingen_US
dc.typeArticleen_US
dc.contributor.corporatenameGeorgia Institute of Technology. Institute for Robotics and Intelligent Machinesen_US
dc.contributor.corporatenameCarnegie-Mellon University. School of Computer Scienceen_US
dc.contributor.corporatenameCarnegie-Mellon University. Robotics Instituteen_US
dc.contributor.corporatenameMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratoryen_US
dc.publisher.originalSAGE
dc.identifier.doi10.1177/0278364914531056
dc.embargo.termsnullen_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record