(Georgia Institute of Technology, 2011-06)
Beall, Chris; Dellaert, Frank; Mahon, Ian; Williams, Stefan B.
In this paper we present a technique to generate
highly accurate reconstructions of underwater structures by
employing bundle adjustment on visual features, rather than
relying on a filtering approach using navigational sensor data
alone. This system improves upon previous work where an
extended information filter was used to estimate the vehicle
trajectory. This filtering technique, while very efficient, suffers
from the shortcoming that linearization errors are irreversibly
incorporated into the vehicle trajectory estimate.
This drawback is overcome by applying smoothing and mapping
to the full problem. In contrast to the filtering approach,
smoothing and mapping techniques solve for the entire vehicle
trajectory and landmark positions at once by performing bundle
adjustment on all the visual measurements taken at each frame.
We formulate a large nonlinear least-squares problem where we
minimize the pixel projection error of each of the landmark
measurements.
The technique is demonstrated on a large-scale underwater
dataset, and it is also shown that superior results are achieved
with smoothing and mapping as compared to the filtering
approach.