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To achieve robust and accurate state estimation for robot navigation, wepropose a novel Visual Inertial Odometry(VIO) algorithm with line features uponthe theory of invariant Kalman filtering and Cubature Kalman Filter (CKF). Incontrast with conventional CKF, the state of the filter is constructed by ahigh dimensional Matrix Lie group and the uncertainty is represented using Liealgebra. To improve the robustness of system in challenging scenes, e.g.low-texture or illumination changing environments, line features are broughtinto the state variable. In the proposed algorithm, exponential mapping of Liealgebra is used to construct the cubature points and the re-projection errorsof lines are built as observation function for updating the state. This methodaccurately describes the system uncertainty in rotation and reduces thelinearization error of system, which extends traditional CKF from Euclideanspace to manifold. It not only inherits the advantages of invariant filteringin consistency, but also avoids the complex Jacobian calculation ofhigh-dimensional matrix. To demonstrate the effectiveness of the proposedalgorithm, we compare it with the state-of-the-art filtering-based VIOalgorithms on Euroc datasets. And the results show that the proposed algorithmis effective in improving accuracy and robustness of estimation.

Invariant Cubature Kalman Filter for Monocular Visual Inertial Odometry with Line Features