(Georgia Institute of Technology, 2014)
Williams, Stephen; Indelman, Vadim; Kaess, Michael; Roberts, Richard; Leonard, John J.; Dellaert, Frank
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.
(Georgia Institute of Technology, 2008-05)
Mottaghi, Roozbeh; Kaess, Michael; Ranganathan, Ananth; Roberts, Richard; Dellaert, Frank
Pose estimation of outdoor robots presents some
distinct challenges due to the various uncertainties in the robot
sensing and action. In particular, global positioning sensors of
outdoor robots do not always work perfectly, causing large drift
in the location estimate of the robot. To overcome this common
problem, we propose a new approach for global localization
using place recognition. First, we learn the location of some
arbitrary key places using odometry measurements and GPS
measurements only at the start and the end of the robot
trajectory. In subsequent runs, when the robot perceives a key
place, our fixed-lag smoother fuses odometry measurements
with the relative location to the key place to improve its pose
estimate. Outdoor mobile robot experiments show that place
recognition measurements significantly improve the estimate of
the smoother in the absence of GPS measurements.