Title:
Concurrent Filtering and Smoothing: A Parallel Architecture for Real-Time Navigation and Full Smoothing

dc.contributor.author Williams, Stephen
dc.contributor.author Indelman, Vadim
dc.contributor.author Kaess, Michael
dc.contributor.author Roberts, Richard
dc.contributor.author Leonard, John J.
dc.contributor.author Dellaert, Frank
dc.contributor.corporatename Georgia Institute of Technology. Institute for Robotics and Intelligent Machines en_US
dc.contributor.corporatename Carnegie-Mellon University. School of Computer Science en_US
dc.contributor.corporatename Carnegie-Mellon University. Robotics Institute en_US
dc.contributor.corporatename Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory en_US
dc.date.accessioned 2015-07-14T17:55:36Z
dc.date.available 2015-07-14T17:55:36Z
dc.date.issued 2014
dc.description © The Author(s) 2014 en_US
dc.description DOI: 10.1177/0278364914531056
dc.description.abstract We 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.embargo.terms null en_US
dc.identifier.citation Williams, 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.doi 10.1177/0278364914531056
dc.identifier.uri http://hdl.handle.net/1853/53685
dc.language.iso en_US en_US
dc.publisher Georgia Institute of Technology en_US
dc.publisher.original SAGE
dc.subject Filtering en_US
dc.subject Information fusion en_US
dc.subject Probabilistic graphical model en_US
dc.subject Real time navigation en_US
dc.subject SLAM en_US
dc.subject Smoothing en_US
dc.title Concurrent Filtering and Smoothing: A Parallel Architecture for Real-Time Navigation and Full Smoothing en_US
dc.type Text
dc.type.genre Article
dspace.entity.type Publication
local.contributor.author Dellaert, Frank
local.contributor.corporatename Institute for Robotics and Intelligent Machines (IRIM)
local.contributor.corporatename College of Computing
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relation.isOrgUnitOfPublication 66259949-abfd-45c2-9dcc-5a6f2c013bcf
relation.isOrgUnitOfPublication c8892b3c-8db6-4b7b-a33a-1b67f7db2021
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