Concurrent filtering and smoothing: A parallel architecture for real-time navigation and full smoothing
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Leonard_Concurrent filtering.pdf
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Author(s)
Williams, Stephen
Indelman, Vadim
Kaess, Michael
Roberts, Richard
Leonard, John Joseph
Dellaert, Frank
Date Issued
July 2014
Journal
The International Journal of Robotics Research
Publisher
Sage Publications
Citation
Williams, S., V. Indelman, M. Kaess, R. Roberts, J. J. Leonard, and F. Dellaert. “Concurrent Filtering and Smoothing: A Parallel Architecture for Real-Time Navigation and Full Smoothing.” The International Journal of Robotics Research 33, no. 12 (July 14, 2014): 1544–1568.
Version
Author's final manuscript
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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Mechanical Engineering
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1177/0278364914531056
