Abstract
Visual-inertial navigation that is able to provide accurate 3D localization in GPS-denied environments has seen popularity in recent years due to the proliferation of cost-effective cameras and inertial measurement units (IMUs). While an extended Kalman filter (EKF) is often used for sensor fusion, factor graph-based optimization has recently revealed its superior performance, which, however, is still compromised by the lack of rigorous IMU preintegration (i.e., integrating IMU measurements in a local frame of reference). To address this issue, in this paper, we analytically derive preintegration based on closed-form solutions of the continuous IMU measurement equations. These expressions allow us to analytically compute the mean, covariance, and bias Jacobians for a set of IMU preintegration factors. These accurate factors are subsequently fused with the visual information via visual-inertial factor graph optimization to provide high-precision trajectory estimates. The proposed method is validated on both Monte Carlo simulations and real-world experiments.
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