Title:
IMU Preintegration on Manifold for Efficient Visual-Inertial Maximum-a-Posteriori Estimation
IMU Preintegration on Manifold for Efficient Visual-Inertial Maximum-a-Posteriori Estimation
Author(s)
Forster, Christian
Carlone, Luca
Dellaert, Frank
Scaramuzza, Davide
Carlone, Luca
Dellaert, Frank
Scaramuzza, Davide
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Abstract
Recent results in monocular visual-inertial navigation (VIN) have shown that optimization-based approaches
outperform filtering methods in terms of accuracy due to their capability to relinearize past states. However, the improvement
comes at the cost of increased computational complexity. In this
paper, we address this issue by preintegrating inertial measurements
between selected keyframes. The preintegration allows us
to accurately summarize hundreds of inertial measurements into
a single relative motion constraint. Our first contribution is a
preintegration theory that properly addresses the manifold structure
of the rotation group and carefully deals with uncertainty
propagation. The measurements are integrated in a local frame,
which eliminates the need to repeat the integration when the
linearization point changes while leaving the opportunity for
belated bias corrections. The second contribution is to show that
the preintegrated IMU model can be seamlessly integrated in a
visual-inertial pipeline under the unifying framework of factor
graphs. This enables the use of a structureless model for visual
measurements, further accelerating the computation. The third
contribution is an extensive evaluation of our monocular VIN
pipeline: experimental results confirm that our system is very fast
and demonstrates superior accuracy with respect to competitive
state-of-the-art filtering and optimization algorithms, including
off-the-shelf systems such as Google Tango [1].
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Date Issued
2015
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Proceedings