(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, 2013-04)
Wu, Allen D.; Johnson, Eric N.; Kaess, Michael; Dellaert, Frank; Chowdhary, Girish
A vision-aided inertial navigation system that enables autonomous flight of an aerial vehicle in GPS-denied
environments is presented. Particularly, feature point information from a monocular vision sensor are used
to bound the drift resulting from integrating accelerations and angular rate measurements from an Inertial
Measurement Unit (IMU) forward in time. An Extended Kalman filter framework is proposed for performing
the tasks of vision-based mapping and navigation separately. When GPS is available, multiple observations of
a single landmark point from the vision sensor are used to estimate the point’s location in inertial space. When
GPS is not available, points that have been sufficiently mapped out can be used for estimating vehicle position
and attitude. Simulation and flight test results of a vehicle operating autonomously in a simplified loss-of-GPS
scenario verify the presented method.
(Georgia Institute of Technology, 2011-01-24)
Ranganathan, Ananth; Dellaert, Frank
We present a novel algorithm for topological mapping, which is the problem of finding the graph structure of an
environment from a sequence of measurements. Our algorithm, called Online Probabilistic Topological Mapping
(OPTM), systematically addresses the problem by constructing the posterior on the space of all possible topologies given
measurements. With each successive measurement, the posterior is updated incrementally using a Rao–Blackwellized
particle filter. We present efficient sampling mechanisms using data-driven proposals and prior distributions on topologies
that further enable OPTM’s operation in an online manner. OPTM can incorporate various sensors seamlessly, as is
demonstrated by our use of appearance, laser, and odometry measurements. OPTM is the first topological mapping
algorithm that is theoretically accurate, systematic, sensor independent, and online, and thus advances the state of the art
significantly. We evaluate the algorithm on a robot in diverse environments.