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On-Manifold Preintegration for Real-Time Visual-Inertial Odometry.pdf
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上传时间: 2020-04-06
详细说明：Abstract: Current approaches for visual-inertial odometry
(VIO) are able to attain highly accurate state estimation via
nonlinear optimization. However, real-time optimization quickly
becomes infeasible as the trajectory grows over time; this problem
is further emphasized by the fact that inertial measurements
come at high rate, hence leading to fast growth of the number
of variables in the optimization. In this paper, we address this
issue by preintegrating inertial measurements between selected
keyframes into single relative motion constraints. Our first
contribution is a preintegration theory that properly addresses
the manifold structure of the rotation group. We formally discuss
the generative measurement model as well as the nature of the
rotation noise and derive the expression for the maximum a
posteriori state estimator. Our theoretical development enables
the computation of all necessary Jacobians for the optimization
and a-posteriori bias correction in analytic form. The second
contribution is to show that the preintegrated IMU model can be
seamlessly integrated into a visual-inertial pipeline under the unifying framework of factor graphs. This enables the application of
incremental-smoothing algorithms and the use of a structureless
model for visual measurements, which avoids optimizing over the
3D points, further accelerating the computation. We perform an
extensive evaluation of our monocular VIO pipeline on real and
simulated datasets. The results confirm that our modelling effort
leads to accurate state estimation in real-time, outperforming
state-of-the-art approaches.
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