  Efficient Visual-Inertial Odometry (VIO) is crucial for payload-constrained
robots. Though modern optimization-based algorithms have achieved superior
accuracy, the MSCKF-based VIO algorithms are still widely demanded for their
efficient and consistent performance. As MSCKF is built upon the conventional
multi-view geometry, the measured residuals are not only related to the state
errors but also related to the feature position errors. To apply EKF fusion, a
projection process is required to remove the feature position error from the
observation model, which can lead to model and accuracy degradation. To obtain
an efficient visual-inertial fusion model, while also preserving the model
consistency, we propose to reconstruct the MSCKF VIO with the novel Pose-Only
(PO) multi-view geometry description. In the newly constructed filter, we have
modeled PO reprojection residuals, which are solely related to the motion
states and thus overcome the requirements of space projection. Moreover, the
new filter does not require any feature position information, which removes the
computational cost and linearization errors brought in by the 3D reconstruction
procedure. We have conducted comprehensive experiments on multiple datasets,
where the proposed method has shown accuracy improvements and consistent
performance in challenging sequences.
