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The Problem of Mobile Sensors: Setting future goals and indicators of progress for SLAM, Workshop in conjunction with the Robotics Science and Systems (RSS) July 2015.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 trans-
form the existing batch algorithm into an efficient incremental
algorithm. In particular, we are able to 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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