Incremental Sparse GP Regression for Continuous-time Trajectory Estimation & Mapping
Author(s)
Yan, Xinyan
Indelman, Vadim
Boots, Byron
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Abstract
Recent work on simultaneous trajectory estimation
and mapping (STEAM) for mobile robots has found success
by representing the trajectory as a Gaussian process. Gaussian
processes can represent a continuous-time trajectory, elegantly
handle asynchronous and sparse measurements, and allow the
robot to query the trajectory to recover its estimated position
at any time of interest. A major drawback of this approach
is that STEAM is formulated as a batch estimation problem.
In this paper we provide the critical extensions necessary to
transform the existing batch algorithm into an extremely efficient
incremental algorithm. In particular, we are able to vastly speed
up the solution time through efficient variable reordering and
incremental sparse updates, which we believe will greatly increase
the practicality of Gaussian process methods for robot mapping
and localization. Finally, we demonstrate the approach and its
advantages on both synthetic and real datasets.
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Date
2015-09
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