Title:
Concurrent Filtering and Smoothing: A Parallel Architecture for Real-Time Navigation and Full Smoothing
Concurrent Filtering and Smoothing: A Parallel Architecture for Real-Time Navigation and Full Smoothing
Author(s)
Williams, Stephen
Indelman, Vadim
Kaess, Michael
Roberts, Richard
Leonard, John J.
Dellaert, Frank
Indelman, Vadim
Kaess, Michael
Roberts, Richard
Leonard, John J.
Dellaert, Frank
Advisor(s)
Editor(s)
Collections
Supplementary to
Permanent Link
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.
Sponsor
Date Issued
2014
Extent
Resource Type
Text
Resource Subtype
Article